Overview

Dataset statistics

Number of variables200
Number of observations8879
Missing cells17640
Missing cells (%)1.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.3 MiB
Average record size in memory1.6 KiB

Variable types

Text2
Boolean45
Categorical153

Alerts

is_current_customer has constant value ""Constant
company_revenue_bucket is highly overall correlated with revenue_Over 10B and 6 other fieldsHigh correlation
country_top is highly overall correlated with country_top_AU and 12 other fieldsHigh correlation
country_top_AU is highly overall correlated with country_top and 1 other fieldsHigh correlation
country_top_BR is highly overall correlated with country_top and 1 other fieldsHigh correlation
country_top_CA is highly overall correlated with country_top and 1 other fieldsHigh correlation
country_top_DE is highly overall correlated with country_top and 1 other fieldsHigh correlation
country_top_FR is highly overall correlated with country_top and 1 other fieldsHigh correlation
country_top_GB is highly overall correlated with country_top and 1 other fieldsHigh correlation
country_top_IN is highly overall correlated with country_top and 1 other fieldsHigh correlation
country_top_Missing is highly overall correlated with country_top and 2 other fieldsHigh correlation
country_top_NL is highly overall correlated with country_top and 1 other fieldsHigh correlation
country_top_Other is highly overall correlated with country_top and 2 other fieldsHigh correlation
country_top_US is highly overall correlated with country_top and 4 other fieldsHigh correlation
crossbeam_product10_customer is highly overall correlated with crossbeam_product11_customer and 22 other fieldsHigh correlation
crossbeam_product11_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product12_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product13_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product14_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product15_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product16_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product17_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product18_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product19_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product1_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product20_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product21_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product22_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product23_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product2_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product3_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product4_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product5_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product6_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product7_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product8_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product9_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
dnb_founded_time_grouped is highly overall correlated with country_top_Missing and 4 other fieldsHigh correlation
founded_After 2000 is highly overall correlated with dnb_founded_time_grouped and 1 other fieldsHigh correlation
founded_Before 2000 is highly overall correlated with dnb_founded_time_groupedHigh correlation
founded_nan is highly overall correlated with dnb_founded_time_grouped and 1 other fieldsHigh correlation
has_crossbeam_data is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
has_hg_data is highly overall correlated with hg_product_100 and 123 other fieldsHigh correlation
hg_product_100 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_101 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_102 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_103 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_104 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_105 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_106 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_107 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_108 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_109 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_110 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_111 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_112 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_113 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_114 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_115 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_116 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_117 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_118 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_119 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_120 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_121 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_122 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_123 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_124 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_125 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_126 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_127 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_128 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_129 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_130 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_131 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_132 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_133 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_134 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_135 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_136 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_137 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_138 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_139 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_140 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_141 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_142 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_143 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_144 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_145 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_146 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_147 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_148 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_27 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_28 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_29 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_30 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_31 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_32 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_33 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_34 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_35 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_36 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_37 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_38 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_39 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_40 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_41 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_42 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_43 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_44 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_45 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_46 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_47 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_48 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_49 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_50 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_51 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_52 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_53 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_54 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_55 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_56 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_57 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_58 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_59 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_60 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_61 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_62 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_63 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_64 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_65 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_66 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_67 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_68 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_69 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_70 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_71 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_72 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_73 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_74 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_75 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_76 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_77 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_78 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_79 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_80 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_81 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_82 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_83 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_84 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_85 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_86 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_87 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_88 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_89 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_90 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_91 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_92 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_93 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_94 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_95 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_96 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_97 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_98 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_99 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
industry_1. Tech - Computer systems design and related services is highly overall correlated with industry_groupedHigh correlation
industry_2. Tech - Software Publisher is highly overall correlated with industry_groupedHigh correlation
industry_3. Finance is highly overall correlated with industry_groupedHigh correlation
industry_4. Consulting is highly overall correlated with industry_groupedHigh correlation
industry_5. Retail is highly overall correlated with industry_groupedHigh correlation
industry_6. Manufacturing is highly overall correlated with industry_groupedHigh correlation
industry_7. Wholesale Trade is highly overall correlated with industry_groupedHigh correlation
industry_8. Healthcare is highly overall correlated with industry_groupedHigh correlation
industry_9. Other is highly overall correlated with industry_groupedHigh correlation
industry_grouped is highly overall correlated with industry_1. Tech - Computer systems design and related services and 9 other fieldsHigh correlation
industry_nan is highly overall correlated with dnb_founded_time_grouped and 1 other fieldsHigh correlation
is_arr_over_12k is highly overall correlated with is_self_serviceHigh correlation
is_self_service is highly overall correlated with is_arr_over_12kHigh correlation
revenue_Over 10B is highly overall correlated with company_revenue_bucketHigh correlation
revenue_Under 100M is highly overall correlated with company_revenue_bucketHigh correlation
revenue_Under 10B is highly overall correlated with company_revenue_bucketHigh correlation
revenue_Under 10M is highly overall correlated with company_revenue_bucketHigh correlation
revenue_Under 1B is highly overall correlated with company_revenue_bucket and 124 other fieldsHigh correlation
revenue_Under 1M is highly overall correlated with company_revenue_bucketHigh correlation
revenue_nan is highly overall correlated with company_revenue_bucket and 124 other fieldsHigh correlation
state is highly overall correlated with country_top and 23 other fieldsHigh correlation
state_top is highly overall correlated with country_top_US and 12 other fieldsHigh correlation
state_top_CA is highly overall correlated with state and 1 other fieldsHigh correlation
state_top_CO is highly overall correlated with state and 1 other fieldsHigh correlation
state_top_FL is highly overall correlated with state and 1 other fieldsHigh correlation
state_top_GA is highly overall correlated with state and 1 other fieldsHigh correlation
state_top_IL is highly overall correlated with state and 1 other fieldsHigh correlation
state_top_MA is highly overall correlated with state and 1 other fieldsHigh correlation
state_top_Missing is highly overall correlated with country_top and 3 other fieldsHigh correlation
state_top_NY is highly overall correlated with state and 1 other fieldsHigh correlation
state_top_Other is highly overall correlated with state and 1 other fieldsHigh correlation
state_top_TX is highly overall correlated with state and 1 other fieldsHigh correlation
state_top_WA is highly overall correlated with state and 1 other fieldsHigh correlation
has_crossbeam_data is highly imbalanced (50.3%)Imbalance
crossbeam_product1_customer is highly imbalanced (65.9%)Imbalance
crossbeam_product2_customer is highly imbalanced (64.2%)Imbalance
crossbeam_product3_customer is highly imbalanced (60.9%)Imbalance
crossbeam_product4_customer is highly imbalanced (67.1%)Imbalance
crossbeam_product5_customer is highly imbalanced (64.3%)Imbalance
crossbeam_product7_customer is highly imbalanced (65.1%)Imbalance
crossbeam_product8_customer is highly imbalanced (52.0%)Imbalance
crossbeam_product9_customer is highly imbalanced (67.9%)Imbalance
crossbeam_product10_customer is highly imbalanced (54.8%)Imbalance
crossbeam_product11_customer is highly imbalanced (55.5%)Imbalance
crossbeam_product12_customer is highly imbalanced (60.4%)Imbalance
crossbeam_product13_customer is highly imbalanced (67.3%)Imbalance
crossbeam_product14_customer is highly imbalanced (64.4%)Imbalance
crossbeam_product16_customer is highly imbalanced (62.5%)Imbalance
crossbeam_product17_customer is highly imbalanced (65.2%)Imbalance
crossbeam_product18_customer is highly imbalanced (56.5%)Imbalance
crossbeam_product19_customer is highly imbalanced (58.7%)Imbalance
crossbeam_product21_customer is highly imbalanced (56.7%)Imbalance
crossbeam_product22_customer is highly imbalanced (64.9%)Imbalance
hg_product_29 is highly imbalanced (52.9%)Imbalance
hg_product_30 is highly imbalanced (53.2%)Imbalance
hg_product_35 is highly imbalanced (54.5%)Imbalance
hg_product_37 is highly imbalanced (50.1%)Imbalance
hg_product_40 is highly imbalanced (55.2%)Imbalance
hg_product_43 is highly imbalanced (54.2%)Imbalance
hg_product_46 is highly imbalanced (55.8%)Imbalance
hg_product_49 is highly imbalanced (51.0%)Imbalance
hg_product_51 is highly imbalanced (50.1%)Imbalance
hg_product_52 is highly imbalanced (52.7%)Imbalance
hg_product_53 is highly imbalanced (53.9%)Imbalance
hg_product_54 is highly imbalanced (55.7%)Imbalance
hg_product_56 is highly imbalanced (55.8%)Imbalance
hg_product_58 is highly imbalanced (56.0%)Imbalance
hg_product_59 is highly imbalanced (56.2%)Imbalance
hg_product_60 is highly imbalanced (55.1%)Imbalance
hg_product_61 is highly imbalanced (56.2%)Imbalance
hg_product_62 is highly imbalanced (51.2%)Imbalance
hg_product_64 is highly imbalanced (55.8%)Imbalance
hg_product_65 is highly imbalanced (51.3%)Imbalance
hg_product_66 is highly imbalanced (52.2%)Imbalance
hg_product_70 is highly imbalanced (51.3%)Imbalance
hg_product_71 is highly imbalanced (54.0%)Imbalance
hg_product_74 is highly imbalanced (53.4%)Imbalance
hg_product_75 is highly imbalanced (51.5%)Imbalance
hg_product_78 is highly imbalanced (52.4%)Imbalance
hg_product_79 is highly imbalanced (50.1%)Imbalance
hg_product_80 is highly imbalanced (52.5%)Imbalance
hg_product_81 is highly imbalanced (50.7%)Imbalance
hg_product_82 is highly imbalanced (56.2%)Imbalance
hg_product_84 is highly imbalanced (53.4%)Imbalance
hg_product_85 is highly imbalanced (56.2%)Imbalance
hg_product_86 is highly imbalanced (54.1%)Imbalance
hg_product_89 is highly imbalanced (51.8%)Imbalance
hg_product_90 is highly imbalanced (50.8%)Imbalance
hg_product_91 is highly imbalanced (56.2%)Imbalance
hg_product_92 is highly imbalanced (55.1%)Imbalance
hg_product_94 is highly imbalanced (56.2%)Imbalance
hg_product_95 is highly imbalanced (52.7%)Imbalance
hg_product_97 is highly imbalanced (55.3%)Imbalance
hg_product_98 is highly imbalanced (54.8%)Imbalance
hg_product_99 is highly imbalanced (54.7%)Imbalance
hg_product_100 is highly imbalanced (53.9%)Imbalance
hg_product_101 is highly imbalanced (53.8%)Imbalance
hg_product_103 is highly imbalanced (50.4%)Imbalance
hg_product_104 is highly imbalanced (54.7%)Imbalance
hg_product_105 is highly imbalanced (51.2%)Imbalance
hg_product_106 is highly imbalanced (55.5%)Imbalance
hg_product_107 is highly imbalanced (55.5%)Imbalance
hg_product_109 is highly imbalanced (56.2%)Imbalance
hg_product_110 is highly imbalanced (55.7%)Imbalance
hg_product_111 is highly imbalanced (56.2%)Imbalance
hg_product_112 is highly imbalanced (52.0%)Imbalance
hg_product_114 is highly imbalanced (52.1%)Imbalance
hg_product_115 is highly imbalanced (55.9%)Imbalance
hg_product_116 is highly imbalanced (54.9%)Imbalance
hg_product_117 is highly imbalanced (55.6%)Imbalance
hg_product_118 is highly imbalanced (56.2%)Imbalance
hg_product_119 is highly imbalanced (51.3%)Imbalance
hg_product_120 is highly imbalanced (54.7%)Imbalance
hg_product_121 is highly imbalanced (56.0%)Imbalance
hg_product_123 is highly imbalanced (52.1%)Imbalance
hg_product_126 is highly imbalanced (54.0%)Imbalance
hg_product_127 is highly imbalanced (54.1%)Imbalance
hg_product_128 is highly imbalanced (56.0%)Imbalance
hg_product_130 is highly imbalanced (55.7%)Imbalance
hg_product_131 is highly imbalanced (55.3%)Imbalance
hg_product_133 is highly imbalanced (55.5%)Imbalance
hg_product_134 is highly imbalanced (55.6%)Imbalance
hg_product_135 is highly imbalanced (56.2%)Imbalance
hg_product_136 is highly imbalanced (56.2%)Imbalance
hg_product_137 is highly imbalanced (54.3%)Imbalance
hg_product_138 is highly imbalanced (55.0%)Imbalance
hg_product_141 is highly imbalanced (53.2%)Imbalance
hg_product_142 is highly imbalanced (53.0%)Imbalance
hg_product_146 is highly imbalanced (54.2%)Imbalance
hg_product_147 is highly imbalanced (53.8%)Imbalance
hg_product_148 is highly imbalanced (55.8%)Imbalance
country_top_AU is highly imbalanced (77.0%)Imbalance
country_top_BR is highly imbalanced (91.1%)Imbalance
country_top_CA is highly imbalanced (76.5%)Imbalance
country_top_DE is highly imbalanced (77.5%)Imbalance
country_top_FR is highly imbalanced (81.8%)Imbalance
country_top_GB is highly imbalanced (59.8%)Imbalance
country_top_IN is highly imbalanced (90.5%)Imbalance
country_top_Missing is highly imbalanced (91.7%)Imbalance
country_top_NL is highly imbalanced (85.8%)Imbalance
state_top_CO is highly imbalanced (87.8%)Imbalance
state_top_FL is highly imbalanced (85.8%)Imbalance
state_top_GA is highly imbalanced (90.2%)Imbalance
state_top_IL is highly imbalanced (84.5%)Imbalance
state_top_MA is highly imbalanced (81.2%)Imbalance
state_top_NY is highly imbalanced (57.0%)Imbalance
state_top_TX is highly imbalanced (78.4%)Imbalance
state_top_WA is highly imbalanced (87.4%)Imbalance
industry_2. Tech - Software Publisher is highly imbalanced (77.5%)Imbalance
industry_3. Finance is highly imbalanced (60.5%)Imbalance
industry_4. Consulting is highly imbalanced (75.1%)Imbalance
industry_5. Retail is highly imbalanced (66.3%)Imbalance
industry_6. Manufacturing is highly imbalanced (63.3%)Imbalance
industry_7. Wholesale Trade is highly imbalanced (77.8%)Imbalance
industry_8. Healthcare is highly imbalanced (78.5%)Imbalance
industry_nan is highly imbalanced (53.9%)Imbalance
revenue_Over 10B is highly imbalanced (89.2%)Imbalance
revenue_Under 100M is highly imbalanced (80.2%)Imbalance
revenue_Under 10B is highly imbalanced (75.8%)Imbalance
revenue_Under 10M is highly imbalanced (94.4%)Imbalance
revenue_Under 1B is highly imbalanced (63.5%)Imbalance
revenue_Under 1M is highly imbalanced (98.2%)Imbalance
founded_Before 2000 is highly imbalanced (56.6%)Imbalance
company_revenue_bucket has 7433 (83.7%) missing valuesMissing
country has 92 (1.0%) missing valuesMissing
state has 3965 (44.7%) missing valuesMissing
industry_grouped has 865 (9.7%) missing valuesMissing
dnb_founded_time_grouped has 5285 (59.5%) missing valuesMissing
account_id has unique valuesUnique

Reproduction

Analysis started2023-12-31 21:27:34.595104
Analysis finished2023-12-31 21:30:03.523951
Duration2 minutes and 28.93 seconds
Software versionydata-profiling vv4.6.3
Download configurationconfig.json

Variables

account_id
Text

UNIQUE 

Distinct8879
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size719.7 KiB
2023-12-31T13:30:03.779367image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters159822
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8879 ?
Unique (%)100.0%

Sample

1st row0011G00000rcirYQAQ
2nd row001PY000002QFboYAG
3rd row001Ho000015TLndIAG
4th row001Ho00001AhSYVIA3
5th row0011G0000121xqjQAA
ValueCountFrequency (%)
0011g00000rciryqaq 1
 
< 0.1%
0011g00000qemnbqai 1
 
< 0.1%
001ho000015qxeyia4 1
 
< 0.1%
001ho000019vnycia2 1
 
< 0.1%
001ho00001938iyiaa 1
 
< 0.1%
0011g00000w5qytqaq 1
 
< 0.1%
0011g00000mgmvbqa0 1
 
< 0.1%
0011g00000tl8jpqak 1
 
< 0.1%
001ho000011rbh4iam 1
 
< 0.1%
001ho000015tlndiag 1
 
< 0.1%
Other values (8869) 8869
99.9%
2023-12-31T13:30:04.319224image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 62034
38.8%
1 18277
 
11.4%
A 10954
 
6.9%
Q 8460
 
5.3%
G 8405
 
5.3%
I 1933
 
1.2%
Y 1912
 
1.2%
3 1525
 
1.0%
n 1511
 
0.9%
C 1480
 
0.9%
Other values (52) 43331
27.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 88119
55.1%
Uppercase Letter 49808
31.2%
Lowercase Letter 21895
 
13.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 10954
22.0%
Q 8460
17.0%
G 8405
16.9%
I 1933
 
3.9%
Y 1912
 
3.8%
C 1480
 
3.0%
K 1298
 
2.6%
E 1245
 
2.5%
H 1227
 
2.5%
S 1214
 
2.4%
Other values (16) 11680
23.5%
Lowercase Letter
ValueCountFrequency (%)
n 1511
 
6.9%
g 1469
 
6.7%
o 1416
 
6.5%
w 1331
 
6.1%
k 1259
 
5.8%
q 1109
 
5.1%
h 1072
 
4.9%
f 925
 
4.2%
u 859
 
3.9%
t 817
 
3.7%
Other values (16) 10127
46.3%
Decimal Number
ValueCountFrequency (%)
0 62034
70.4%
1 18277
 
20.7%
3 1525
 
1.7%
7 1385
 
1.6%
2 1131
 
1.3%
4 900
 
1.0%
9 794
 
0.9%
8 736
 
0.8%
5 731
 
0.8%
6 606
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 88119
55.1%
Latin 71703
44.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 10954
 
15.3%
Q 8460
 
11.8%
G 8405
 
11.7%
I 1933
 
2.7%
Y 1912
 
2.7%
n 1511
 
2.1%
C 1480
 
2.1%
g 1469
 
2.0%
o 1416
 
2.0%
w 1331
 
1.9%
Other values (42) 32832
45.8%
Common
ValueCountFrequency (%)
0 62034
70.4%
1 18277
 
20.7%
3 1525
 
1.7%
7 1385
 
1.6%
2 1131
 
1.3%
4 900
 
1.0%
9 794
 
0.9%
8 736
 
0.8%
5 731
 
0.8%
6 606
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 159822
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 62034
38.8%
1 18277
 
11.4%
A 10954
 
6.9%
Q 8460
 
5.3%
G 8405
 
5.3%
I 1933
 
1.2%
Y 1912
 
1.2%
3 1525
 
1.0%
n 1511
 
0.9%
C 1480
 
0.9%
Other values (52) 43331
27.1%

is_current_customer
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.0 KiB
True
8879 
ValueCountFrequency (%)
True 8879
100.0%
2023-12-31T13:30:04.540297image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

is_self_service
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size592.6 KiB
1.0
5843 
-1.0
3036 

Length

Max length4
Median length3
Mean length3.3419304
Min length3

Characters and Unicode

Total characters29673
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 5843
65.8%
-1.0 3036
34.2%

Length

2023-12-31T13:30:04.716677image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:04.886215image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 8879
100.0%

Most occurring characters

ValueCountFrequency (%)
1 8879
29.9%
. 8879
29.9%
0 8879
29.9%
- 3036
 
10.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
59.8%
Other Punctuation 8879
29.9%
Dash Punctuation 3036
 
10.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 8879
50.0%
0 8879
50.0%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3036
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 29673
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 8879
29.9%
. 8879
29.9%
0 8879
29.9%
- 3036
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29673
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 8879
29.9%
. 8879
29.9%
0 8879
29.9%
- 3036
 
10.2%

is_arr_over_12k
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size595.0 KiB
-1.0
5462 
1.0
3417 

Length

Max length4
Median length4
Mean length3.6151594
Min length3

Characters and Unicode

Total characters32099
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1.0
2nd row-1.0
3rd row-1.0
4th row-1.0
5th row-1.0

Common Values

ValueCountFrequency (%)
-1.0 5462
61.5%
1.0 3417
38.5%

Length

2023-12-31T13:30:05.074335image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:05.246707image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 8879
100.0%

Most occurring characters

ValueCountFrequency (%)
1 8879
27.7%
. 8879
27.7%
0 8879
27.7%
- 5462
17.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
55.3%
Other Punctuation 8879
27.7%
Dash Punctuation 5462
 
17.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 8879
50.0%
0 8879
50.0%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5462
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 32099
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 8879
27.7%
. 8879
27.7%
0 8879
27.7%
- 5462
17.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32099
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 8879
27.7%
. 8879
27.7%
0 8879
27.7%
- 5462
17.0%

company_revenue_bucket
Categorical

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)0.4%
Missing7433
Missing (%)83.7%
Memory size626.7 KiB
Under 1B
620 
Under 10B
354 
Under 100M
273 
Over 10B
127 
Under 10M
 
57

Length

Max length10
Median length8
Mean length8.6618257
Min length8

Characters and Unicode

Total characters12525
Distinct characters12
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnder 1B
2nd rowUnder 10B
3rd rowUnder 100M
4th rowUnder 1B
5th rowOver 10B

Common Values

ValueCountFrequency (%)
Under 1B 620
 
7.0%
Under 10B 354
 
4.0%
Under 100M 273
 
3.1%
Over 10B 127
 
1.4%
Under 10M 57
 
0.6%
Under 1M 15
 
0.2%
(Missing) 7433
83.7%

Length

2023-12-31T13:30:05.441304image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:05.633328image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
under 1319
45.6%
1b 620
21.4%
10b 481
 
16.6%
100m 273
 
9.4%
over 127
 
4.4%
10m 57
 
2.0%
1m 15
 
0.5%

Most occurring characters

ValueCountFrequency (%)
e 1446
11.5%
r 1446
11.5%
1446
11.5%
1 1446
11.5%
U 1319
10.5%
n 1319
10.5%
d 1319
10.5%
B 1101
8.8%
0 1084
8.7%
M 345
 
2.8%
Other values (2) 254
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5657
45.2%
Uppercase Letter 2892
23.1%
Decimal Number 2530
20.2%
Space Separator 1446
 
11.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1446
25.6%
r 1446
25.6%
n 1319
23.3%
d 1319
23.3%
v 127
 
2.2%
Uppercase Letter
ValueCountFrequency (%)
U 1319
45.6%
B 1101
38.1%
M 345
 
11.9%
O 127
 
4.4%
Decimal Number
ValueCountFrequency (%)
1 1446
57.2%
0 1084
42.8%
Space Separator
ValueCountFrequency (%)
1446
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8549
68.3%
Common 3976
31.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1446
16.9%
r 1446
16.9%
U 1319
15.4%
n 1319
15.4%
d 1319
15.4%
B 1101
12.9%
M 345
 
4.0%
O 127
 
1.5%
v 127
 
1.5%
Common
ValueCountFrequency (%)
1446
36.4%
1 1446
36.4%
0 1084
27.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12525
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1446
11.5%
r 1446
11.5%
1446
11.5%
1 1446
11.5%
U 1319
10.5%
n 1319
10.5%
d 1319
10.5%
B 1101
8.8%
0 1084
8.7%
M 345
 
2.8%
Other values (2) 254
 
2.0%

country
Text

MISSING 

Distinct103
Distinct (%)1.2%
Missing92
Missing (%)1.0%
Memory size578.5 KiB
2023-12-31T13:30:05.878919image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters17574
Distinct characters28
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)0.3%

Sample

1st rowNL
2nd rowUS
3rd rowGB
4th rowGB
5th rowNZ
ValueCountFrequency (%)
us 4983
56.7%
gb 710
 
8.1%
ca 341
 
3.9%
au 332
 
3.8%
de 322
 
3.7%
fr 244
 
2.8%
nl 178
 
2.0%
jp 145
 
1.7%
in 108
 
1.2%
br 100
 
1.1%
Other values (92) 1324
 
15.1%
2023-12-31T13:30:06.337514image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 5342
30.4%
S 5259
29.9%
B 880
 
5.0%
G 818
 
4.7%
A 808
 
4.6%
E 665
 
3.8%
C 487
 
2.8%
N 459
 
2.6%
R 416
 
2.4%
D 400
 
2.3%
Other values (18) 2040
 
11.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 17572
> 99.9%
Lowercase Letter 2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 5342
30.4%
S 5259
29.9%
B 880
 
5.0%
G 818
 
4.7%
A 808
 
4.6%
E 665
 
3.8%
C 487
 
2.8%
N 459
 
2.6%
R 416
 
2.4%
D 400
 
2.3%
Other values (16) 2038
 
11.6%
Lowercase Letter
ValueCountFrequency (%)
c 1
50.0%
o 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 17574
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 5342
30.4%
S 5259
29.9%
B 880
 
5.0%
G 818
 
4.7%
A 808
 
4.6%
E 665
 
3.8%
C 487
 
2.8%
N 459
 
2.6%
R 416
 
2.4%
D 400
 
2.3%
Other values (18) 2040
 
11.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17574
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 5342
30.4%
S 5259
29.9%
B 880
 
5.0%
G 818
 
4.7%
A 808
 
4.6%
E 665
 
3.8%
C 487
 
2.8%
N 459
 
2.6%
R 416
 
2.4%
D 400
 
2.3%
Other values (18) 2040
 
11.6%

state
Categorical

HIGH CORRELATION  MISSING 

Distinct50
Distinct (%)1.0%
Missing3965
Missing (%)44.7%
Memory size600.3 KiB
CA
1472 
NY
781 
TX
305 
MA
255 
IL
200 
Other values (45)
1901 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters9828
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st rowMA
2nd rowIL
3rd rowCO
4th rowNY
5th rowCA

Common Values

ValueCountFrequency (%)
CA 1472
 
16.6%
NY 781
 
8.8%
TX 305
 
3.4%
MA 255
 
2.9%
IL 200
 
2.3%
FL 178
 
2.0%
WA 153
 
1.7%
CO 147
 
1.7%
UT 127
 
1.4%
GA 113
 
1.3%
Other values (40) 1183
 
13.3%
(Missing) 3965
44.7%

Length

2023-12-31T13:30:06.562453image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ca 1472
30.0%
ny 781
15.9%
tx 305
 
6.2%
ma 255
 
5.2%
il 200
 
4.1%
fl 178
 
3.6%
wa 153
 
3.1%
co 147
 
3.0%
ut 127
 
2.6%
ga 113
 
2.3%
Other values (40) 1183
24.1%

Most occurring characters

ValueCountFrequency (%)
A 2280
23.2%
C 1744
17.7%
N 1145
11.7%
Y 807
 
8.2%
T 520
 
5.3%
M 474
 
4.8%
L 412
 
4.2%
I 372
 
3.8%
O 357
 
3.6%
X 305
 
3.1%
Other values (14) 1412
14.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 9828
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 2280
23.2%
C 1744
17.7%
N 1145
11.7%
Y 807
 
8.2%
T 520
 
5.3%
M 474
 
4.8%
L 412
 
4.2%
I 372
 
3.8%
O 357
 
3.6%
X 305
 
3.1%
Other values (14) 1412
14.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 9828
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 2280
23.2%
C 1744
17.7%
N 1145
11.7%
Y 807
 
8.2%
T 520
 
5.3%
M 474
 
4.8%
L 412
 
4.2%
I 372
 
3.8%
O 357
 
3.6%
X 305
 
3.1%
Other values (14) 1412
14.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9828
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 2280
23.2%
C 1744
17.7%
N 1145
11.7%
Y 807
 
8.2%
T 520
 
5.3%
M 474
 
4.8%
L 412
 
4.2%
I 372
 
3.8%
O 357
 
3.6%
X 305
 
3.1%
Other values (14) 1412
14.4%

industry_grouped
Categorical

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)0.1%
Missing865
Missing (%)9.7%
Memory size710.8 KiB
9. Other
3515 
1. Tech - Computer systems design and related services
1318 
3. Finance
692 
6. Manufacturing
623 
5. Retail
554 
Other values (4)
1312 

Length

Max length54
Median length28
Mean length18.049913
Min length8

Characters and Unicode

Total characters144652
Distinct characters43
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9. Other
2nd row5. Retail
3rd row9. Other
4th row9. Other
5th row3. Finance

Common Values

ValueCountFrequency (%)
9. Other 3515
39.6%
1. Tech - Computer systems design and related services 1318
 
14.8%
3. Finance 692
 
7.8%
6. Manufacturing 623
 
7.0%
5. Retail 554
 
6.2%
4. Consulting 368
 
4.1%
2. Tech - Software Publisher 323
 
3.6%
7. Wholesale Trade 317
 
3.6%
8. Healthcare 304
 
3.4%
(Missing) 865
 
9.7%

Length

2023-12-31T13:30:06.750746image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:06.953480image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
9 3515
13.2%
other 3515
13.2%
tech 1641
 
6.2%
1641
 
6.2%
1 1318
 
5.0%
computer 1318
 
5.0%
systems 1318
 
5.0%
design 1318
 
5.0%
and 1318
 
5.0%
related 1318
 
5.0%
Other values (17) 8320
31.3%

Most occurring characters

ValueCountFrequency (%)
18526
 
12.8%
e 17833
 
12.3%
t 9641
 
6.7%
r 9359
 
6.5%
s 8916
 
6.2%
. 8014
 
5.5%
a 6693
 
4.6%
h 6100
 
4.2%
n 6002
 
4.1%
i 5196
 
3.6%
Other values (33) 48372
33.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 98162
67.9%
Space Separator 18526
 
12.8%
Uppercase Letter 10295
 
7.1%
Other Punctuation 8014
 
5.5%
Decimal Number 8014
 
5.5%
Dash Punctuation 1641
 
1.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 17833
18.2%
t 9641
9.8%
r 9359
9.5%
s 8916
9.1%
a 6693
 
6.8%
h 6100
 
6.2%
n 6002
 
6.1%
i 5196
 
5.3%
c 4578
 
4.7%
d 4271
 
4.4%
Other values (11) 19573
19.9%
Uppercase Letter
ValueCountFrequency (%)
O 3515
34.1%
T 1958
19.0%
C 1686
16.4%
F 692
 
6.7%
M 623
 
6.1%
R 554
 
5.4%
S 323
 
3.1%
P 323
 
3.1%
W 317
 
3.1%
H 304
 
3.0%
Decimal Number
ValueCountFrequency (%)
9 3515
43.9%
1 1318
 
16.4%
3 692
 
8.6%
6 623
 
7.8%
5 554
 
6.9%
4 368
 
4.6%
2 323
 
4.0%
7 317
 
4.0%
8 304
 
3.8%
Space Separator
ValueCountFrequency (%)
18526
100.0%
Other Punctuation
ValueCountFrequency (%)
. 8014
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1641
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 108457
75.0%
Common 36195
 
25.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 17833
16.4%
t 9641
 
8.9%
r 9359
 
8.6%
s 8916
 
8.2%
a 6693
 
6.2%
h 6100
 
5.6%
n 6002
 
5.5%
i 5196
 
4.8%
c 4578
 
4.2%
d 4271
 
3.9%
Other values (21) 29868
27.5%
Common
ValueCountFrequency (%)
18526
51.2%
. 8014
22.1%
9 3515
 
9.7%
- 1641
 
4.5%
1 1318
 
3.6%
3 692
 
1.9%
6 623
 
1.7%
5 554
 
1.5%
4 368
 
1.0%
2 323
 
0.9%
Other values (2) 621
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 144652
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
18526
 
12.8%
e 17833
 
12.3%
t 9641
 
6.7%
r 9359
 
6.5%
s 8916
 
6.2%
. 8014
 
5.5%
a 6693
 
4.6%
h 6100
 
4.2%
n 6002
 
4.1%
i 5196
 
3.6%
Other values (33) 48372
33.4%

has_crossbeam_data
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.0 KiB
True
7912 
False
967 
ValueCountFrequency (%)
True 7912
89.1%
False 967
 
10.9%
2023-12-31T13:30:07.171875image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

crossbeam_product1_customer
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size597.3 KiB
-1.0
7877 
0.0
953 
1.0
 
49

Length

Max length4
Median length4
Mean length3.8871495
Min length3

Characters and Unicode

Total characters34514
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
-1.0 7877
88.7%
0.0 953
 
10.7%
1.0 49
 
0.6%

Length

2023-12-31T13:30:07.348929image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:07.520472image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 7926
89.3%
0.0 953
 
10.7%

Most occurring characters

ValueCountFrequency (%)
0 9832
28.5%
. 8879
25.7%
1 7926
23.0%
- 7877
22.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
51.5%
Other Punctuation 8879
25.7%
Dash Punctuation 7877
22.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9832
55.4%
1 7926
44.6%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7877
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 34514
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9832
28.5%
. 8879
25.7%
1 7926
23.0%
- 7877
22.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34514
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9832
28.5%
. 8879
25.7%
1 7926
23.0%
- 7877
22.8%

crossbeam_product2_customer
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size597.3 KiB
-1.0
7843 
0.0
953 
1.0
 
83

Length

Max length4
Median length4
Mean length3.8833202
Min length3

Characters and Unicode

Total characters34480
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
-1.0 7843
88.3%
0.0 953
 
10.7%
1.0 83
 
0.9%

Length

2023-12-31T13:30:07.706748image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:08.108225image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 7926
89.3%
0.0 953
 
10.7%

Most occurring characters

ValueCountFrequency (%)
0 9832
28.5%
. 8879
25.8%
1 7926
23.0%
- 7843
22.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
51.5%
Other Punctuation 8879
25.8%
Dash Punctuation 7843
22.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9832
55.4%
1 7926
44.6%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7843
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 34480
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9832
28.5%
. 8879
25.8%
1 7926
23.0%
- 7843
22.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34480
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9832
28.5%
. 8879
25.8%
1 7926
23.0%
- 7843
22.7%

crossbeam_product3_customer
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size597.2 KiB
-1.0
7766 
0.0
953 
1.0
 
160

Length

Max length4
Median length4
Mean length3.874648
Min length3

Characters and Unicode

Total characters34403
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
-1.0 7766
87.5%
0.0 953
 
10.7%
1.0 160
 
1.8%

Length

2023-12-31T13:30:08.283970image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:08.454702image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 7926
89.3%
0.0 953
 
10.7%

Most occurring characters

ValueCountFrequency (%)
0 9832
28.6%
. 8879
25.8%
1 7926
23.0%
- 7766
22.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
51.6%
Other Punctuation 8879
25.8%
Dash Punctuation 7766
22.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9832
55.4%
1 7926
44.6%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7766
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 34403
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9832
28.6%
. 8879
25.8%
1 7926
23.0%
- 7766
22.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34403
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9832
28.6%
. 8879
25.8%
1 7926
23.0%
- 7766
22.6%

crossbeam_product4_customer
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size597.3 KiB
-1.0
7898 
0.0
953 
1.0
 
28

Length

Max length4
Median length4
Mean length3.8895146
Min length3

Characters and Unicode

Total characters34535
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
-1.0 7898
89.0%
0.0 953
 
10.7%
1.0 28
 
0.3%

Length

2023-12-31T13:30:08.634705image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:08.803956image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 7926
89.3%
0.0 953
 
10.7%

Most occurring characters

ValueCountFrequency (%)
0 9832
28.5%
. 8879
25.7%
1 7926
23.0%
- 7898
22.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
51.4%
Other Punctuation 8879
25.7%
Dash Punctuation 7898
22.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9832
55.4%
1 7926
44.6%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7898
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 34535
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9832
28.5%
. 8879
25.7%
1 7926
23.0%
- 7898
22.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34535
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9832
28.5%
. 8879
25.7%
1 7926
23.0%
- 7898
22.9%

crossbeam_product5_customer
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size597.3 KiB
-1.0
7845 
0.0
953 
1.0
 
81

Length

Max length4
Median length4
Mean length3.8835454
Min length3

Characters and Unicode

Total characters34482
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
-1.0 7845
88.4%
0.0 953
 
10.7%
1.0 81
 
0.9%

Length

2023-12-31T13:30:08.981578image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:09.150172image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 7926
89.3%
0.0 953
 
10.7%

Most occurring characters

ValueCountFrequency (%)
0 9832
28.5%
. 8879
25.7%
1 7926
23.0%
- 7845
22.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
51.5%
Other Punctuation 8879
25.7%
Dash Punctuation 7845
22.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9832
55.4%
1 7926
44.6%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7845
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 34482
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9832
28.5%
. 8879
25.7%
1 7926
23.0%
- 7845
22.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34482
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9832
28.5%
. 8879
25.7%
1 7926
23.0%
- 7845
22.8%

crossbeam_product6_customer
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size596.6 KiB
-1.0
7184 
0.0
953 
1.0
742 

Length

Max length4
Median length4
Mean length3.8091001
Min length3

Characters and Unicode

Total characters33821
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
-1.0 7184
80.9%
0.0 953
 
10.7%
1.0 742
 
8.4%

Length

2023-12-31T13:30:09.342288image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:09.518157image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 7926
89.3%
0.0 953
 
10.7%

Most occurring characters

ValueCountFrequency (%)
0 9832
29.1%
. 8879
26.3%
1 7926
23.4%
- 7184
21.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
52.5%
Other Punctuation 8879
26.3%
Dash Punctuation 7184
21.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9832
55.4%
1 7926
44.6%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7184
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 33821
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9832
29.1%
. 8879
26.3%
1 7926
23.4%
- 7184
21.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33821
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9832
29.1%
. 8879
26.3%
1 7926
23.4%
- 7184
21.2%

crossbeam_product7_customer
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size597.3 KiB
-1.0
7860 
0.0
953 
1.0
 
66

Length

Max length4
Median length4
Mean length3.8852348
Min length3

Characters and Unicode

Total characters34497
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
-1.0 7860
88.5%
0.0 953
 
10.7%
1.0 66
 
0.7%

Length

2023-12-31T13:30:09.698365image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:09.864815image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 7926
89.3%
0.0 953
 
10.7%

Most occurring characters

ValueCountFrequency (%)
0 9832
28.5%
. 8879
25.7%
1 7926
23.0%
- 7860
22.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
51.5%
Other Punctuation 8879
25.7%
Dash Punctuation 7860
22.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9832
55.4%
1 7926
44.6%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7860
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 34497
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9832
28.5%
. 8879
25.7%
1 7926
23.0%
- 7860
22.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34497
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9832
28.5%
. 8879
25.7%
1 7926
23.0%
- 7860
22.8%

crossbeam_product8_customer
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size596.9 KiB
-1.0
7502 
0.0
953 
1.0
 
424

Length

Max length4
Median length4
Mean length3.844915
Min length3

Characters and Unicode

Total characters34139
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
-1.0 7502
84.5%
0.0 953
 
10.7%
1.0 424
 
4.8%

Length

2023-12-31T13:30:10.048984image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:10.221859image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 7926
89.3%
0.0 953
 
10.7%

Most occurring characters

ValueCountFrequency (%)
0 9832
28.8%
. 8879
26.0%
1 7926
23.2%
- 7502
22.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
52.0%
Other Punctuation 8879
26.0%
Dash Punctuation 7502
22.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9832
55.4%
1 7926
44.6%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7502
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 34139
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9832
28.8%
. 8879
26.0%
1 7926
23.2%
- 7502
22.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34139
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9832
28.8%
. 8879
26.0%
1 7926
23.2%
- 7502
22.0%

crossbeam_product9_customer
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size597.3 KiB
-1.0
7912 
0.0
953 
1.0
 
14

Length

Max length4
Median length4
Mean length3.8910913
Min length3

Characters and Unicode

Total characters34549
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
-1.0 7912
89.1%
0.0 953
 
10.7%
1.0 14
 
0.2%

Length

2023-12-31T13:30:10.401412image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:10.566992image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 7926
89.3%
0.0 953
 
10.7%

Most occurring characters

ValueCountFrequency (%)
0 9832
28.5%
. 8879
25.7%
1 7926
22.9%
- 7912
22.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
51.4%
Other Punctuation 8879
25.7%
Dash Punctuation 7912
22.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9832
55.4%
1 7926
44.6%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7912
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 34549
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9832
28.5%
. 8879
25.7%
1 7926
22.9%
- 7912
22.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34549
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9832
28.5%
. 8879
25.7%
1 7926
22.9%
- 7912
22.9%

crossbeam_product10_customer
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size597.0 KiB
-1.0
7592 
0.0
953 
1.0
 
334

Length

Max length4
Median length4
Mean length3.8550512
Min length3

Characters and Unicode

Total characters34229
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
-1.0 7592
85.5%
0.0 953
 
10.7%
1.0 334
 
3.8%

Length

2023-12-31T13:30:10.750258image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:10.921879image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 7926
89.3%
0.0 953
 
10.7%

Most occurring characters

ValueCountFrequency (%)
0 9832
28.7%
. 8879
25.9%
1 7926
23.2%
- 7592
22.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
51.9%
Other Punctuation 8879
25.9%
Dash Punctuation 7592
22.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9832
55.4%
1 7926
44.6%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7592
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 34229
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9832
28.7%
. 8879
25.9%
1 7926
23.2%
- 7592
22.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34229
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9832
28.7%
. 8879
25.9%
1 7926
23.2%
- 7592
22.2%

crossbeam_product11_customer
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size597.1 KiB
-1.0
7614 
0.0
953 
1.0
 
312

Length

Max length4
Median length4
Mean length3.857529
Min length3

Characters and Unicode

Total characters34251
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
-1.0 7614
85.8%
0.0 953
 
10.7%
1.0 312
 
3.5%

Length

2023-12-31T13:30:11.107206image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:11.279908image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 7926
89.3%
0.0 953
 
10.7%

Most occurring characters

ValueCountFrequency (%)
0 9832
28.7%
. 8879
25.9%
1 7926
23.1%
- 7614
22.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
51.8%
Other Punctuation 8879
25.9%
Dash Punctuation 7614
22.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9832
55.4%
1 7926
44.6%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7614
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 34251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9832
28.7%
. 8879
25.9%
1 7926
23.1%
- 7614
22.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9832
28.7%
. 8879
25.9%
1 7926
23.1%
- 7614
22.2%

crossbeam_product12_customer
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size597.2 KiB
-1.0
7753 
0.0
953 
1.0
 
173

Length

Max length4
Median length4
Mean length3.8731839
Min length3

Characters and Unicode

Total characters34390
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
-1.0 7753
87.3%
0.0 953
 
10.7%
1.0 173
 
1.9%

Length

2023-12-31T13:30:11.476396image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:11.643444image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 7926
89.3%
0.0 953
 
10.7%

Most occurring characters

ValueCountFrequency (%)
0 9832
28.6%
. 8879
25.8%
1 7926
23.0%
- 7753
22.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
51.6%
Other Punctuation 8879
25.8%
Dash Punctuation 7753
22.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9832
55.4%
1 7926
44.6%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7753
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 34390
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9832
28.6%
. 8879
25.8%
1 7926
23.0%
- 7753
22.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34390
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9832
28.6%
. 8879
25.8%
1 7926
23.0%
- 7753
22.5%

crossbeam_product13_customer
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size597.3 KiB
-1.0
7902 
0.0
953 
1.0
 
24

Length

Max length4
Median length4
Mean length3.8899651
Min length3

Characters and Unicode

Total characters34539
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
-1.0 7902
89.0%
0.0 953
 
10.7%
1.0 24
 
0.3%

Length

2023-12-31T13:30:11.824718image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:11.993788image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 7926
89.3%
0.0 953
 
10.7%

Most occurring characters

ValueCountFrequency (%)
0 9832
28.5%
. 8879
25.7%
1 7926
22.9%
- 7902
22.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
51.4%
Other Punctuation 8879
25.7%
Dash Punctuation 7902
22.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9832
55.4%
1 7926
44.6%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7902
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 34539
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9832
28.5%
. 8879
25.7%
1 7926
22.9%
- 7902
22.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34539
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9832
28.5%
. 8879
25.7%
1 7926
22.9%
- 7902
22.9%

crossbeam_product14_customer
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size597.3 KiB
-1.0
7846 
0.0
953 
1.0
 
80

Length

Max length4
Median length4
Mean length3.8836581
Min length3

Characters and Unicode

Total characters34483
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
-1.0 7846
88.4%
0.0 953
 
10.7%
1.0 80
 
0.9%

Length

2023-12-31T13:30:12.172072image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:12.337179image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 7926
89.3%
0.0 953
 
10.7%

Most occurring characters

ValueCountFrequency (%)
0 9832
28.5%
. 8879
25.7%
1 7926
23.0%
- 7846
22.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
51.5%
Other Punctuation 8879
25.7%
Dash Punctuation 7846
22.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9832
55.4%
1 7926
44.6%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7846
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 34483
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9832
28.5%
. 8879
25.7%
1 7926
23.0%
- 7846
22.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34483
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9832
28.5%
. 8879
25.7%
1 7926
23.0%
- 7846
22.8%

crossbeam_product15_customer
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size596.7 KiB
-1.0
7258 
0.0
953 
1.0
 
668

Length

Max length4
Median length4
Mean length3.8174344
Min length3

Characters and Unicode

Total characters33895
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
-1.0 7258
81.7%
0.0 953
 
10.7%
1.0 668
 
7.5%

Length

2023-12-31T13:30:12.523218image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:12.692271image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 7926
89.3%
0.0 953
 
10.7%

Most occurring characters

ValueCountFrequency (%)
0 9832
29.0%
. 8879
26.2%
1 7926
23.4%
- 7258
21.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
52.4%
Other Punctuation 8879
26.2%
Dash Punctuation 7258
21.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9832
55.4%
1 7926
44.6%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7258
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 33895
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9832
29.0%
. 8879
26.2%
1 7926
23.4%
- 7258
21.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33895
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9832
29.0%
. 8879
26.2%
1 7926
23.4%
- 7258
21.4%

crossbeam_product16_customer
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size597.2 KiB
-1.0
7804 
0.0
953 
1.0
 
122

Length

Max length4
Median length4
Mean length3.8789278
Min length3

Characters and Unicode

Total characters34441
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
-1.0 7804
87.9%
0.0 953
 
10.7%
1.0 122
 
1.4%

Length

2023-12-31T13:30:12.869969image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:13.043386image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 7926
89.3%
0.0 953
 
10.7%

Most occurring characters

ValueCountFrequency (%)
0 9832
28.5%
. 8879
25.8%
1 7926
23.0%
- 7804
22.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
51.6%
Other Punctuation 8879
25.8%
Dash Punctuation 7804
22.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9832
55.4%
1 7926
44.6%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7804
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 34441
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9832
28.5%
. 8879
25.8%
1 7926
23.0%
- 7804
22.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34441
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9832
28.5%
. 8879
25.8%
1 7926
23.0%
- 7804
22.7%

crossbeam_product17_customer
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size597.3 KiB
-1.0
7863 
0.0
953 
1.0
 
63

Length

Max length4
Median length4
Mean length3.8855727
Min length3

Characters and Unicode

Total characters34500
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
-1.0 7863
88.6%
0.0 953
 
10.7%
1.0 63
 
0.7%

Length

2023-12-31T13:30:13.252646image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:13.428321image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 7926
89.3%
0.0 953
 
10.7%

Most occurring characters

ValueCountFrequency (%)
0 9832
28.5%
. 8879
25.7%
1 7926
23.0%
- 7863
22.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
51.5%
Other Punctuation 8879
25.7%
Dash Punctuation 7863
22.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9832
55.4%
1 7926
44.6%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7863
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 34500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9832
28.5%
. 8879
25.7%
1 7926
23.0%
- 7863
22.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9832
28.5%
. 8879
25.7%
1 7926
23.0%
- 7863
22.8%

crossbeam_product18_customer
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size597.1 KiB
-1.0
7644 
0.0
953 
1.0
 
282

Length

Max length4
Median length4
Mean length3.8609078
Min length3

Characters and Unicode

Total characters34281
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
-1.0 7644
86.1%
0.0 953
 
10.7%
1.0 282
 
3.2%

Length

2023-12-31T13:30:13.606393image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:13.776112image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 7926
89.3%
0.0 953
 
10.7%

Most occurring characters

ValueCountFrequency (%)
0 9832
28.7%
. 8879
25.9%
1 7926
23.1%
- 7644
22.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
51.8%
Other Punctuation 8879
25.9%
Dash Punctuation 7644
22.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9832
55.4%
1 7926
44.6%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7644
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 34281
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9832
28.7%
. 8879
25.9%
1 7926
23.1%
- 7644
22.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34281
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9832
28.7%
. 8879
25.9%
1 7926
23.1%
- 7644
22.3%

crossbeam_product19_customer
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size597.1 KiB
-1.0
7706 
0.0
953 
1.0
 
220

Length

Max length4
Median length4
Mean length3.8678905
Min length3

Characters and Unicode

Total characters34343
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
-1.0 7706
86.8%
0.0 953
 
10.7%
1.0 220
 
2.5%

Length

2023-12-31T13:30:13.956012image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:14.121086image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 7926
89.3%
0.0 953
 
10.7%

Most occurring characters

ValueCountFrequency (%)
0 9832
28.6%
. 8879
25.9%
1 7926
23.1%
- 7706
22.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
51.7%
Other Punctuation 8879
25.9%
Dash Punctuation 7706
22.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9832
55.4%
1 7926
44.6%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7706
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 34343
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9832
28.6%
. 8879
25.9%
1 7926
23.1%
- 7706
22.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34343
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9832
28.6%
. 8879
25.9%
1 7926
23.1%
- 7706
22.4%

crossbeam_product20_customer
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size595.2 KiB
-1.0
5672 
1.0
2254 
0.0
953 

Length

Max length4
Median length4
Mean length3.6388107
Min length3

Characters and Unicode

Total characters32309
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
-1.0 5672
63.9%
1.0 2254
 
25.4%
0.0 953
 
10.7%

Length

2023-12-31T13:30:14.308488image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:14.479343image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 7926
89.3%
0.0 953
 
10.7%

Most occurring characters

ValueCountFrequency (%)
0 9832
30.4%
. 8879
27.5%
1 7926
24.5%
- 5672
17.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
55.0%
Other Punctuation 8879
27.5%
Dash Punctuation 5672
 
17.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9832
55.4%
1 7926
44.6%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5672
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 32309
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9832
30.4%
. 8879
27.5%
1 7926
24.5%
- 5672
17.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32309
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9832
30.4%
. 8879
27.5%
1 7926
24.5%
- 5672
17.6%

crossbeam_product21_customer
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size597.1 KiB
-1.0
7651 
0.0
953 
1.0
 
275

Length

Max length4
Median length4
Mean length3.8616961
Min length3

Characters and Unicode

Total characters34288
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
-1.0 7651
86.2%
0.0 953
 
10.7%
1.0 275
 
3.1%

Length

2023-12-31T13:30:14.664979image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:14.834568image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 7926
89.3%
0.0 953
 
10.7%

Most occurring characters

ValueCountFrequency (%)
0 9832
28.7%
. 8879
25.9%
1 7926
23.1%
- 7651
22.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
51.8%
Other Punctuation 8879
25.9%
Dash Punctuation 7651
22.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9832
55.4%
1 7926
44.6%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7651
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 34288
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9832
28.7%
. 8879
25.9%
1 7926
23.1%
- 7651
22.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34288
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9832
28.7%
. 8879
25.9%
1 7926
23.1%
- 7651
22.3%

crossbeam_product22_customer
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size597.3 KiB
-1.0
7857 
0.0
953 
1.0
 
69

Length

Max length4
Median length4
Mean length3.8848969
Min length3

Characters and Unicode

Total characters34494
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
-1.0 7857
88.5%
0.0 953
 
10.7%
1.0 69
 
0.8%

Length

2023-12-31T13:30:15.013566image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:15.182117image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 7926
89.3%
0.0 953
 
10.7%

Most occurring characters

ValueCountFrequency (%)
0 9832
28.5%
. 8879
25.7%
1 7926
23.0%
- 7857
22.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
51.5%
Other Punctuation 8879
25.7%
Dash Punctuation 7857
22.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9832
55.4%
1 7926
44.6%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7857
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 34494
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9832
28.5%
. 8879
25.7%
1 7926
23.0%
- 7857
22.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34494
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9832
28.5%
. 8879
25.7%
1 7926
23.0%
- 7857
22.8%

crossbeam_product23_customer
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size595.2 KiB
-1.0
5726 
1.0
2200 
0.0
953 

Length

Max length4
Median length4
Mean length3.6448924
Min length3

Characters and Unicode

Total characters32363
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
-1.0 5726
64.5%
1.0 2200
 
24.8%
0.0 953
 
10.7%

Length

2023-12-31T13:30:15.365301image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:15.540896image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 7926
89.3%
0.0 953
 
10.7%

Most occurring characters

ValueCountFrequency (%)
0 9832
30.4%
. 8879
27.4%
1 7926
24.5%
- 5726
17.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
54.9%
Other Punctuation 8879
27.4%
Dash Punctuation 5726
 
17.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9832
55.4%
1 7926
44.6%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5726
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 32363
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9832
30.4%
. 8879
27.4%
1 7926
24.5%
- 5726
17.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32363
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9832
30.4%
. 8879
27.4%
1 7926
24.5%
- 5726
17.7%

dnb_founded_time_grouped
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.1%
Missing5285
Missing (%)59.5%
Memory size635.6 KiB
After 2000
2801 
Before 2000
793 

Length

Max length11
Median length10
Mean length10.220646
Min length10

Characters and Unicode

Total characters36733
Distinct characters10
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAfter 2000
2nd rowAfter 2000
3rd rowAfter 2000
4th rowAfter 2000
5th rowBefore 2000

Common Values

ValueCountFrequency (%)
After 2000 2801
31.5%
Before 2000 793
 
8.9%
(Missing) 5285
59.5%

Length

2023-12-31T13:30:15.729244image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:15.896488image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
2000 3594
50.0%
after 2801
39.0%
before 793
 
11.0%

Most occurring characters

ValueCountFrequency (%)
0 10782
29.4%
e 4387
11.9%
f 3594
 
9.8%
r 3594
 
9.8%
3594
 
9.8%
2 3594
 
9.8%
A 2801
 
7.6%
t 2801
 
7.6%
B 793
 
2.2%
o 793
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15169
41.3%
Decimal Number 14376
39.1%
Space Separator 3594
 
9.8%
Uppercase Letter 3594
 
9.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 4387
28.9%
f 3594
23.7%
r 3594
23.7%
t 2801
18.5%
o 793
 
5.2%
Decimal Number
ValueCountFrequency (%)
0 10782
75.0%
2 3594
 
25.0%
Uppercase Letter
ValueCountFrequency (%)
A 2801
77.9%
B 793
 
22.1%
Space Separator
ValueCountFrequency (%)
3594
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 18763
51.1%
Common 17970
48.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 4387
23.4%
f 3594
19.2%
r 3594
19.2%
A 2801
14.9%
t 2801
14.9%
B 793
 
4.2%
o 793
 
4.2%
Common
ValueCountFrequency (%)
0 10782
60.0%
3594
 
20.0%
2 3594
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36733
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10782
29.4%
e 4387
11.9%
f 3594
 
9.8%
r 3594
 
9.8%
3594
 
9.8%
2 3594
 
9.8%
A 2801
 
7.6%
t 2801
 
7.6%
B 793
 
2.2%
o 793
 
2.2%

has_hg_data
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.0 KiB
False
7311 
True
1568 
ValueCountFrequency (%)
False 7311
82.3%
True 1568
 
17.7%
2023-12-31T13:30:16.046111image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

hg_product_27
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1648 

Length

Max length4
Median length3
Mean length3.1856065
Min length3

Characters and Unicode

Total characters28285
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1648
 
18.6%

Length

2023-12-31T13:30:16.225549image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:16.406582image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
- 1648
 
5.8%
1 1648
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.8%
Other Punctuation 8879
31.4%
Dash Punctuation 1648
 
5.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1648
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28285
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
- 1648
 
5.8%
1 1648
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28285
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
- 1648
 
5.8%
1 1648
 
5.8%

hg_product_28
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size590.9 KiB
0.0
7231 
-1.0
1284 
1.0
 
364

Length

Max length4
Median length3
Mean length3.1446109
Min length3

Characters and Unicode

Total characters27921
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1284
 
14.5%
1.0 364
 
4.1%

Length

2023-12-31T13:30:16.596067image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:16.776046image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.7%
. 8879
31.8%
1 1648
 
5.9%
- 1284
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
63.6%
Other Punctuation 8879
31.8%
Dash Punctuation 1284
 
4.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1284
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27921
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.7%
. 8879
31.8%
1 1648
 
5.9%
- 1284
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27921
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.7%
. 8879
31.8%
1 1648
 
5.9%
- 1284
 
4.6%

hg_product_29
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.1 KiB
0.0
7231 
-1.0
1563 
1.0
 
85

Length

Max length4
Median length3
Mean length3.1760333
Min length3

Characters and Unicode

Total characters28200
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1563
 
17.6%
1.0 85
 
1.0%

Length

2023-12-31T13:30:16.965714image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:17.142204image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.1%
. 8879
31.5%
1 1648
 
5.8%
- 1563
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
63.0%
Other Punctuation 8879
31.5%
Dash Punctuation 1563
 
5.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1563
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28200
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.1%
. 8879
31.5%
1 1648
 
5.8%
- 1563
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.1%
. 8879
31.5%
1 1648
 
5.8%
- 1563
 
5.5%

hg_product_30
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1573 
1.0
 
75

Length

Max length4
Median length3
Mean length3.1771596
Min length3

Characters and Unicode

Total characters28210
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1573
 
17.7%
1.0 75
 
0.8%

Length

2023-12-31T13:30:17.332661image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:17.518654image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.1%
. 8879
31.5%
1 1648
 
5.8%
- 1573
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.9%
Other Punctuation 8879
31.5%
Dash Punctuation 1573
 
5.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1573
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.1%
. 8879
31.5%
1 1648
 
5.8%
- 1573
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.1%
. 8879
31.5%
1 1648
 
5.8%
- 1573
 
5.6%

hg_product_31
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size590.6 KiB
0.0
7231 
-1.0
952 
1.0
 
696

Length

Max length4
Median length3
Mean length3.1072193
Min length3

Characters and Unicode

Total characters27589
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 952
 
10.7%
1.0 696
 
7.8%

Length

2023-12-31T13:30:17.710452image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:17.878857image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
58.4%
. 8879
32.2%
1 1648
 
6.0%
- 952
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
64.4%
Other Punctuation 8879
32.2%
Dash Punctuation 952
 
3.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 952
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27589
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
58.4%
. 8879
32.2%
1 1648
 
6.0%
- 952
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27589
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
58.4%
. 8879
32.2%
1 1648
 
6.0%
- 952
 
3.5%

hg_product_32
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size590.2 KiB
0.0
7231 
1.0
1046 
-1.0
 
602

Length

Max length4
Median length3
Mean length3.0678004
Min length3

Characters and Unicode

Total characters27239
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1046
 
11.8%
-1.0 602
 
6.8%

Length

2023-12-31T13:30:18.059626image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:18.224388image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
59.1%
. 8879
32.6%
1 1648
 
6.1%
- 602
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
65.2%
Other Punctuation 8879
32.6%
Dash Punctuation 602
 
2.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 602
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27239
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
59.1%
. 8879
32.6%
1 1648
 
6.1%
- 602
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27239
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
59.1%
. 8879
32.6%
1 1648
 
6.1%
- 602
 
2.2%

hg_product_33
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size590.6 KiB
0.0
7231 
-1.0
1004 
1.0
 
644

Length

Max length4
Median length3
Mean length3.1130758
Min length3

Characters and Unicode

Total characters27641
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1004
 
11.3%
1.0 644
 
7.3%

Length

2023-12-31T13:30:18.402579image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:18.568144image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
58.3%
. 8879
32.1%
1 1648
 
6.0%
- 1004
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
64.2%
Other Punctuation 8879
32.1%
Dash Punctuation 1004
 
3.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1004
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27641
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
58.3%
. 8879
32.1%
1 1648
 
6.0%
- 1004
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27641
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
58.3%
. 8879
32.1%
1 1648
 
6.0%
- 1004
 
3.6%

hg_product_34
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size590.5 KiB
0.0
7231 
-1.0
856 
1.0
792 

Length

Max length4
Median length3
Mean length3.0964073
Min length3

Characters and Unicode

Total characters27493
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 856
 
9.6%
1.0 792
 
8.9%

Length

2023-12-31T13:30:18.744635image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:18.915012image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
58.6%
. 8879
32.3%
1 1648
 
6.0%
- 856
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
64.6%
Other Punctuation 8879
32.3%
Dash Punctuation 856
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 856
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27493
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
58.6%
. 8879
32.3%
1 1648
 
6.0%
- 856
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27493
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
58.6%
. 8879
32.3%
1 1648
 
6.0%
- 856
 
3.1%

hg_product_35
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1611 
1.0
 
37

Length

Max length4
Median length3
Mean length3.1814394
Min length3

Characters and Unicode

Total characters28248
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1611
 
18.1%
1.0 37
 
0.4%

Length

2023-12-31T13:30:19.103040image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:19.276558image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1611
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.9%
Other Punctuation 8879
31.4%
Dash Punctuation 1611
 
5.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1611
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28248
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1611
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28248
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1611
 
5.7%

hg_product_36
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size590.9 KiB
0.0
7231 
-1.0
1268 
1.0
 
380

Length

Max length4
Median length3
Mean length3.1428089
Min length3

Characters and Unicode

Total characters27905
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1268
 
14.3%
1.0 380
 
4.3%

Length

2023-12-31T13:30:19.460538image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:19.635151image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.7%
. 8879
31.8%
1 1648
 
5.9%
- 1268
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
63.6%
Other Punctuation 8879
31.8%
Dash Punctuation 1268
 
4.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1268
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27905
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.7%
. 8879
31.8%
1 1648
 
5.9%
- 1268
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27905
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.7%
. 8879
31.8%
1 1648
 
5.9%
- 1268
 
4.5%

hg_product_37
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.0 KiB
0.0
7231 
-1.0
1447 
1.0
 
201

Length

Max length4
Median length3
Mean length3.1629688
Min length3

Characters and Unicode

Total characters28084
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1447
 
16.3%
1.0 201
 
2.3%

Length

2023-12-31T13:30:19.818974image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:19.989233image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.4%
. 8879
31.6%
1 1648
 
5.9%
- 1447
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
63.2%
Other Punctuation 8879
31.6%
Dash Punctuation 1447
 
5.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1447
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28084
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.4%
. 8879
31.6%
1 1648
 
5.9%
- 1447
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28084
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.4%
. 8879
31.6%
1 1648
 
5.9%
- 1447
 
5.2%

hg_product_38
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size590.9 KiB
0.0
7231 
-1.0
1266 
1.0
 
382

Length

Max length4
Median length3
Mean length3.1425836
Min length3

Characters and Unicode

Total characters27903
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1266
 
14.3%
1.0 382
 
4.3%

Length

2023-12-31T13:30:20.173812image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:20.344266image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.7%
. 8879
31.8%
1 1648
 
5.9%
- 1266
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
63.6%
Other Punctuation 8879
31.8%
Dash Punctuation 1266
 
4.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1266
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27903
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.7%
. 8879
31.8%
1 1648
 
5.9%
- 1266
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27903
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.7%
. 8879
31.8%
1 1648
 
5.9%
- 1266
 
4.5%

hg_product_39
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size590.9 KiB
0.0
7231 
-1.0
1325 
1.0
 
323

Length

Max length4
Median length3
Mean length3.1492285
Min length3

Characters and Unicode

Total characters27962
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1325
 
14.9%
1.0 323
 
3.6%

Length

2023-12-31T13:30:20.536072image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:20.711813image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.6%
. 8879
31.8%
1 1648
 
5.9%
- 1325
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
63.5%
Other Punctuation 8879
31.8%
Dash Punctuation 1325
 
4.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1325
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27962
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.6%
. 8879
31.8%
1 1648
 
5.9%
- 1325
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27962
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.6%
. 8879
31.8%
1 1648
 
5.9%
- 1325
 
4.7%

hg_product_40
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1628 
1.0
 
20

Length

Max length4
Median length3
Mean length3.183354
Min length3

Characters and Unicode

Total characters28265
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1628
 
18.3%
1.0 20
 
0.2%

Length

2023-12-31T13:30:20.901572image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:21.090921image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1628
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.8%
Other Punctuation 8879
31.4%
Dash Punctuation 1628
 
5.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1628
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28265
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1628
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28265
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1628
 
5.8%

hg_product_41
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.0 KiB
0.0
7231 
-1.0
1400 
1.0
 
248

Length

Max length4
Median length3
Mean length3.1576754
Min length3

Characters and Unicode

Total characters28037
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1400
 
15.8%
1.0 248
 
2.8%

Length

2023-12-31T13:30:21.284177image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:21.657478image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.5%
. 8879
31.7%
1 1648
 
5.9%
- 1400
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
63.3%
Other Punctuation 8879
31.7%
Dash Punctuation 1400
 
5.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1400
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28037
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.5%
. 8879
31.7%
1 1648
 
5.9%
- 1400
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28037
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.5%
. 8879
31.7%
1 1648
 
5.9%
- 1400
 
5.0%

hg_product_42
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.0 KiB
0.0
7231 
-1.0
1384 
1.0
 
264

Length

Max length4
Median length3
Mean length3.1558734
Min length3

Characters and Unicode

Total characters28021
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1384
 
15.6%
1.0 264
 
3.0%

Length

2023-12-31T13:30:21.839780image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:22.012820image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.5%
. 8879
31.7%
1 1648
 
5.9%
- 1384
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
63.4%
Other Punctuation 8879
31.7%
Dash Punctuation 1384
 
4.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1384
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28021
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.5%
. 8879
31.7%
1 1648
 
5.9%
- 1384
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28021
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.5%
. 8879
31.7%
1 1648
 
5.9%
- 1384
 
4.9%

hg_product_43
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1603 
1.0
 
45

Length

Max length4
Median length3
Mean length3.1805383
Min length3

Characters and Unicode

Total characters28240
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1603
 
18.1%
1.0 45
 
0.5%

Length

2023-12-31T13:30:22.196647image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:22.379058image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1603
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.9%
Other Punctuation 8879
31.4%
Dash Punctuation 1603
 
5.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1603
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1603
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1603
 
5.7%

hg_product_44
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.0 KiB
0.0
7231 
-1.0
1390 
1.0
 
258

Length

Max length4
Median length3
Mean length3.1565492
Min length3

Characters and Unicode

Total characters28027
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1390
 
15.7%
1.0 258
 
2.9%

Length

2023-12-31T13:30:22.567421image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:22.740896image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.5%
. 8879
31.7%
1 1648
 
5.9%
- 1390
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
63.4%
Other Punctuation 8879
31.7%
Dash Punctuation 1390
 
5.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1390
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28027
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.5%
. 8879
31.7%
1 1648
 
5.9%
- 1390
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28027
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.5%
. 8879
31.7%
1 1648
 
5.9%
- 1390
 
5.0%

hg_product_45
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size590.7 KiB
0.0
7231 
-1.0
1152 
1.0
 
496

Length

Max length4
Median length3
Mean length3.1297443
Min length3

Characters and Unicode

Total characters27789
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1152
 
13.0%
1.0 496
 
5.6%

Length

2023-12-31T13:30:22.931372image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:23.105761image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
58.0%
. 8879
32.0%
1 1648
 
5.9%
- 1152
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
63.9%
Other Punctuation 8879
32.0%
Dash Punctuation 1152
 
4.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1152
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27789
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
58.0%
. 8879
32.0%
1 1648
 
5.9%
- 1152
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27789
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
58.0%
. 8879
32.0%
1 1648
 
5.9%
- 1152
 
4.1%

hg_product_46
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1640 
1.0
 
8

Length

Max length4
Median length3
Mean length3.1847055
Min length3

Characters and Unicode

Total characters28277
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1640
 
18.5%
1.0 8
 
0.1%

Length

2023-12-31T13:30:23.297801image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:23.474180image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1640
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.8%
Other Punctuation 8879
31.4%
Dash Punctuation 1640
 
5.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1640
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28277
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1640
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28277
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1640
 
5.8%

hg_product_47
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size590.4 KiB
0.0
7231 
1.0
895 
-1.0
753 

Length

Max length4
Median length3
Mean length3.0848068
Min length3

Characters and Unicode

Total characters27390
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
1.0 895
 
10.1%
-1.0 753
 
8.5%

Length

2023-12-31T13:30:23.655430image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:23.834299image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
58.8%
. 8879
32.4%
1 1648
 
6.0%
- 753
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
64.8%
Other Punctuation 8879
32.4%
Dash Punctuation 753
 
2.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 753
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27390
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
58.8%
. 8879
32.4%
1 1648
 
6.0%
- 753
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27390
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
58.8%
. 8879
32.4%
1 1648
 
6.0%
- 753
 
2.7%

hg_product_48
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size589.9 KiB
0.0
7231 
1.0
1348 
-1.0
 
300

Length

Max length4
Median length3
Mean length3.0337876
Min length3

Characters and Unicode

Total characters26937
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1348
 
15.2%
-1.0 300
 
3.4%

Length

2023-12-31T13:30:24.023673image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:24.196616image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
59.8%
. 8879
33.0%
1 1648
 
6.1%
- 300
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
65.9%
Other Punctuation 8879
33.0%
Dash Punctuation 300
 
1.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 300
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 26937
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
59.8%
. 8879
33.0%
1 1648
 
6.1%
- 300
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26937
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
59.8%
. 8879
33.0%
1 1648
 
6.1%
- 300
 
1.1%

hg_product_49
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.1 KiB
0.0
7231 
-1.0
1492 
1.0
 
156

Length

Max length4
Median length3
Mean length3.1680369
Min length3

Characters and Unicode

Total characters28129
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1492
 
16.8%
1.0 156
 
1.8%

Length

2023-12-31T13:30:24.387395image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:24.561707image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.3%
. 8879
31.6%
1 1648
 
5.9%
- 1492
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
63.1%
Other Punctuation 8879
31.6%
Dash Punctuation 1492
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1492
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28129
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.3%
. 8879
31.6%
1 1648
 
5.9%
- 1492
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28129
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.3%
. 8879
31.6%
1 1648
 
5.9%
- 1492
 
5.3%

hg_product_50
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size590.6 KiB
0.0
7231 
-1.0
1010 
1.0
 
638

Length

Max length4
Median length3
Mean length3.1137515
Min length3

Characters and Unicode

Total characters27647
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1010
 
11.4%
1.0 638
 
7.2%

Length

2023-12-31T13:30:24.745810image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:24.914847image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
58.3%
. 8879
32.1%
1 1648
 
6.0%
- 1010
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
64.2%
Other Punctuation 8879
32.1%
Dash Punctuation 1010
 
3.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1010
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27647
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
58.3%
. 8879
32.1%
1 1648
 
6.0%
- 1010
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27647
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
58.3%
. 8879
32.1%
1 1648
 
6.0%
- 1010
 
3.7%

hg_product_51
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.0 KiB
0.0
7231 
-1.0
1448 
1.0
 
200

Length

Max length4
Median length3
Mean length3.1630814
Min length3

Characters and Unicode

Total characters28085
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1448
 
16.3%
1.0 200
 
2.3%

Length

2023-12-31T13:30:25.103979image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:25.277666image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.4%
. 8879
31.6%
1 1648
 
5.9%
- 1448
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
63.2%
Other Punctuation 8879
31.6%
Dash Punctuation 1448
 
5.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1448
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28085
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.4%
. 8879
31.6%
1 1648
 
5.9%
- 1448
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28085
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.4%
. 8879
31.6%
1 1648
 
5.9%
- 1448
 
5.2%

hg_product_52
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.1 KiB
0.0
7231 
-1.0
1558 
1.0
 
90

Length

Max length4
Median length3
Mean length3.1754702
Min length3

Characters and Unicode

Total characters28195
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1558
 
17.5%
1.0 90
 
1.0%

Length

2023-12-31T13:30:25.467306image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:25.641374image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.1%
. 8879
31.5%
1 1648
 
5.8%
- 1558
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
63.0%
Other Punctuation 8879
31.5%
Dash Punctuation 1558
 
5.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1558
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28195
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.1%
. 8879
31.5%
1 1648
 
5.8%
- 1558
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28195
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.1%
. 8879
31.5%
1 1648
 
5.8%
- 1558
 
5.5%

hg_product_53
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1594 
1.0
 
54

Length

Max length4
Median length3
Mean length3.1795247
Min length3

Characters and Unicode

Total characters28231
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1594
 
18.0%
1.0 54
 
0.6%

Length

2023-12-31T13:30:25.829745image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:26.005712image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.1%
. 8879
31.5%
1 1648
 
5.8%
- 1594
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.9%
Other Punctuation 8879
31.5%
Dash Punctuation 1594
 
5.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1594
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28231
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.1%
. 8879
31.5%
1 1648
 
5.8%
- 1594
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28231
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.1%
. 8879
31.5%
1 1648
 
5.8%
- 1594
 
5.6%

hg_product_54
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1637 
1.0
 
11

Length

Max length4
Median length3
Mean length3.1843676
Min length3

Characters and Unicode

Total characters28274
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1637
 
18.4%
1.0 11
 
0.1%

Length

2023-12-31T13:30:26.193798image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:26.374711image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1637
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.8%
Other Punctuation 8879
31.4%
Dash Punctuation 1637
 
5.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1637
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28274
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1637
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28274
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1637
 
5.8%

hg_product_55
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size590.5 KiB
0.0
7231 
-1.0
915 
1.0
733 

Length

Max length4
Median length3
Mean length3.1030521
Min length3

Characters and Unicode

Total characters27552
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 915
 
10.3%
1.0 733
 
8.3%

Length

2023-12-31T13:30:26.555005image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:26.726836image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
58.5%
. 8879
32.2%
1 1648
 
6.0%
- 915
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
64.5%
Other Punctuation 8879
32.2%
Dash Punctuation 915
 
3.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 915
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27552
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
58.5%
. 8879
32.2%
1 1648
 
6.0%
- 915
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27552
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
58.5%
. 8879
32.2%
1 1648
 
6.0%
- 915
 
3.3%

hg_product_56
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1639 
1.0
 
9

Length

Max length4
Median length3
Mean length3.1845929
Min length3

Characters and Unicode

Total characters28276
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1639
 
18.5%
1.0 9
 
0.1%

Length

2023-12-31T13:30:26.917763image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:27.095710image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1639
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.8%
Other Punctuation 8879
31.4%
Dash Punctuation 1639
 
5.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1639
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28276
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1639
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28276
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1639
 
5.8%

hg_product_57
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size590.9 KiB
0.0
7231 
-1.0
1297 
1.0
 
351

Length

Max length4
Median length3
Mean length3.146075
Min length3

Characters and Unicode

Total characters27934
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1297
 
14.6%
1.0 351
 
4.0%

Length

2023-12-31T13:30:27.292986image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:27.470064image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.7%
. 8879
31.8%
1 1648
 
5.9%
- 1297
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
63.6%
Other Punctuation 8879
31.8%
Dash Punctuation 1297
 
4.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1297
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27934
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.7%
. 8879
31.8%
1 1648
 
5.9%
- 1297
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27934
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.7%
. 8879
31.8%
1 1648
 
5.9%
- 1297
 
4.6%

hg_product_58
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1644 
1.0
 
4

Length

Max length4
Median length3
Mean length3.185156
Min length3

Characters and Unicode

Total characters28281
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1644
 
18.5%
1.0 4
 
< 0.1%

Length

2023-12-31T13:30:27.663832image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:27.841713image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1644
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.8%
Other Punctuation 8879
31.4%
Dash Punctuation 1644
 
5.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1644
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28281
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1644
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28281
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1644
 
5.8%

hg_product_59
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1646 
1.0
 
2

Length

Max length4
Median length3
Mean length3.1853812
Min length3

Characters and Unicode

Total characters28283
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1646
 
18.5%
1.0 2
 
< 0.1%

Length

2023-12-31T13:30:28.031980image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:28.206842image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1646
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.8%
Other Punctuation 8879
31.4%
Dash Punctuation 1646
 
5.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1646
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28283
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1646
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28283
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1646
 
5.8%

hg_product_60
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1625 
1.0
 
23

Length

Max length4
Median length3
Mean length3.1830161
Min length3

Characters and Unicode

Total characters28262
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1625
 
18.3%
1.0 23
 
0.3%

Length

2023-12-31T13:30:28.394633image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:28.576088image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1625
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.8%
Other Punctuation 8879
31.4%
Dash Punctuation 1625
 
5.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1625
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28262
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1625
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28262
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1625
 
5.7%

hg_product_61
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1647 
1.0
 
1

Length

Max length4
Median length3
Mean length3.1854939
Min length3

Characters and Unicode

Total characters28284
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1647
 
18.5%
1.0 1
 
< 0.1%

Length

2023-12-31T13:30:28.765714image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:28.943024image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1647
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.8%
Other Punctuation 8879
31.4%
Dash Punctuation 1647
 
5.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1647
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28284
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1647
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28284
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1647
 
5.8%

hg_product_62
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.1 KiB
0.0
7231 
-1.0
1497 
1.0
 
151

Length

Max length4
Median length3
Mean length3.1686001
Min length3

Characters and Unicode

Total characters28134
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1497
 
16.9%
1.0 151
 
1.7%

Length

2023-12-31T13:30:29.130196image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:29.306097image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.3%
. 8879
31.6%
1 1648
 
5.9%
- 1497
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
63.1%
Other Punctuation 8879
31.6%
Dash Punctuation 1497
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1497
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28134
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.3%
. 8879
31.6%
1 1648
 
5.9%
- 1497
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28134
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.3%
. 8879
31.6%
1 1648
 
5.9%
- 1497
 
5.3%

hg_product_63
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size590.9 KiB
0.0
7231 
-1.0
1305 
1.0
 
343

Length

Max length4
Median length3
Mean length3.146976
Min length3

Characters and Unicode

Total characters27942
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1305
 
14.7%
1.0 343
 
3.9%

Length

2023-12-31T13:30:29.492116image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:29.668082image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.7%
. 8879
31.8%
1 1648
 
5.9%
- 1305
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
63.6%
Other Punctuation 8879
31.8%
Dash Punctuation 1305
 
4.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1305
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27942
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.7%
. 8879
31.8%
1 1648
 
5.9%
- 1305
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27942
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.7%
. 8879
31.8%
1 1648
 
5.9%
- 1305
 
4.7%

hg_product_64
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1640 
1.0
 
8

Length

Max length4
Median length3
Mean length3.1847055
Min length3

Characters and Unicode

Total characters28277
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1640
 
18.5%
1.0 8
 
0.1%

Length

2023-12-31T13:30:29.857447image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:30.031733image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1640
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.8%
Other Punctuation 8879
31.4%
Dash Punctuation 1640
 
5.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1640
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28277
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1640
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28277
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1640
 
5.8%

hg_product_65
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.1 KiB
0.0
7231 
-1.0
1504 
1.0
 
144

Length

Max length4
Median length3
Mean length3.1693884
Min length3

Characters and Unicode

Total characters28141
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1504
 
16.9%
1.0 144
 
1.6%

Length

2023-12-31T13:30:30.223193image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:30.398181image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.2%
. 8879
31.6%
1 1648
 
5.9%
- 1504
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
63.1%
Other Punctuation 8879
31.6%
Dash Punctuation 1504
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1504
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28141
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.2%
. 8879
31.6%
1 1648
 
5.9%
- 1504
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28141
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.2%
. 8879
31.6%
1 1648
 
5.9%
- 1504
 
5.3%

hg_product_66
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.1 KiB
0.0
7231 
-1.0
1540 
1.0
 
108

Length

Max length4
Median length3
Mean length3.173443
Min length3

Characters and Unicode

Total characters28177
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1540
 
17.3%
1.0 108
 
1.2%

Length

2023-12-31T13:30:30.588929image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:30.762515image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.2%
. 8879
31.5%
1 1648
 
5.8%
- 1540
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
63.0%
Other Punctuation 8879
31.5%
Dash Punctuation 1540
 
5.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1540
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.2%
. 8879
31.5%
1 1648
 
5.8%
- 1540
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.2%
. 8879
31.5%
1 1648
 
5.8%
- 1540
 
5.5%

hg_product_67
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size590.1 KiB
0.0
7231 
1.0
1201 
-1.0
 
447

Length

Max length4
Median length3
Mean length3.0503435
Min length3

Characters and Unicode

Total characters27084
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1201
 
13.5%
-1.0 447
 
5.0%

Length

2023-12-31T13:30:30.944871image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:31.112924image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
59.5%
. 8879
32.8%
1 1648
 
6.1%
- 447
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
65.6%
Other Punctuation 8879
32.8%
Dash Punctuation 447
 
1.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 447
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27084
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
59.5%
. 8879
32.8%
1 1648
 
6.1%
- 447
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27084
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
59.5%
. 8879
32.8%
1 1648
 
6.1%
- 447
 
1.7%

hg_product_68
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.0 KiB
0.0
7231 
-1.0
1432 
1.0
 
216

Length

Max length4
Median length3
Mean length3.1612794
Min length3

Characters and Unicode

Total characters28069
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1432
 
16.1%
1.0 216
 
2.4%

Length

2023-12-31T13:30:31.300561image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:31.477468image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.4%
. 8879
31.6%
1 1648
 
5.9%
- 1432
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
63.3%
Other Punctuation 8879
31.6%
Dash Punctuation 1432
 
5.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1432
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28069
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.4%
. 8879
31.6%
1 1648
 
5.9%
- 1432
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28069
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.4%
. 8879
31.6%
1 1648
 
5.9%
- 1432
 
5.1%

hg_product_69
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size590.7 KiB
0.0
7231 
-1.0
1068 
1.0
 
580

Length

Max length4
Median length3
Mean length3.1202838
Min length3

Characters and Unicode

Total characters27705
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1068
 
12.0%
1.0 580
 
6.5%

Length

2023-12-31T13:30:31.658687image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:31.829076image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
58.1%
. 8879
32.0%
1 1648
 
5.9%
- 1068
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
64.1%
Other Punctuation 8879
32.0%
Dash Punctuation 1068
 
3.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1068
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27705
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
58.1%
. 8879
32.0%
1 1648
 
5.9%
- 1068
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27705
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
58.1%
. 8879
32.0%
1 1648
 
5.9%
- 1068
 
3.9%

hg_product_70
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.1 KiB
0.0
7231 
-1.0
1505 
1.0
 
143

Length

Max length4
Median length3
Mean length3.1695011
Min length3

Characters and Unicode

Total characters28142
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1505
 
17.0%
1.0 143
 
1.6%

Length

2023-12-31T13:30:32.016851image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:32.194793image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.2%
. 8879
31.6%
1 1648
 
5.9%
- 1505
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
63.1%
Other Punctuation 8879
31.6%
Dash Punctuation 1505
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1505
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28142
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.2%
. 8879
31.6%
1 1648
 
5.9%
- 1505
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28142
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.2%
. 8879
31.6%
1 1648
 
5.9%
- 1505
 
5.3%

hg_product_71
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1597 
1.0
 
51

Length

Max length4
Median length3
Mean length3.1798626
Min length3

Characters and Unicode

Total characters28234
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1597
 
18.0%
1.0 51
 
0.6%

Length

2023-12-31T13:30:32.383980image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:32.568164image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.1%
. 8879
31.4%
1 1648
 
5.8%
- 1597
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.9%
Other Punctuation 8879
31.4%
Dash Punctuation 1597
 
5.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1597
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28234
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.1%
. 8879
31.4%
1 1648
 
5.8%
- 1597
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28234
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.1%
. 8879
31.4%
1 1648
 
5.8%
- 1597
 
5.7%

hg_product_72
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size590.9 KiB
0.0
7231 
-1.0
1359 
1.0
 
289

Length

Max length4
Median length3
Mean length3.1530578
Min length3

Characters and Unicode

Total characters27996
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1359
 
15.3%
1.0 289
 
3.3%

Length

2023-12-31T13:30:32.778357image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:32.977101image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.5%
. 8879
31.7%
1 1648
 
5.9%
- 1359
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
63.4%
Other Punctuation 8879
31.7%
Dash Punctuation 1359
 
4.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1359
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27996
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.5%
. 8879
31.7%
1 1648
 
5.9%
- 1359
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27996
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.5%
. 8879
31.7%
1 1648
 
5.9%
- 1359
 
4.9%

hg_product_73
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size590.2 KiB
0.0
7231 
1.0
1067 
-1.0
 
581

Length

Max length4
Median length3
Mean length3.0654353
Min length3

Characters and Unicode

Total characters27218
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1067
 
12.0%
-1.0 581
 
6.5%

Length

2023-12-31T13:30:33.165907image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:33.337243image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
59.2%
. 8879
32.6%
1 1648
 
6.1%
- 581
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
65.2%
Other Punctuation 8879
32.6%
Dash Punctuation 581
 
2.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 581
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27218
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
59.2%
. 8879
32.6%
1 1648
 
6.1%
- 581
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27218
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
59.2%
. 8879
32.6%
1 1648
 
6.1%
- 581
 
2.1%

hg_product_74
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1580 
1.0
 
68

Length

Max length4
Median length3
Mean length3.177948
Min length3

Characters and Unicode

Total characters28217
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1580
 
17.8%
1.0 68
 
0.8%

Length

2023-12-31T13:30:33.535372image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:33.714455image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.1%
. 8879
31.5%
1 1648
 
5.8%
- 1580
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.9%
Other Punctuation 8879
31.5%
Dash Punctuation 1580
 
5.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1580
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28217
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.1%
. 8879
31.5%
1 1648
 
5.8%
- 1580
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28217
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.1%
. 8879
31.5%
1 1648
 
5.8%
- 1580
 
5.6%

hg_product_75
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.1 KiB
0.0
7231 
-1.0
1511 
1.0
 
137

Length

Max length4
Median length3
Mean length3.1701768
Min length3

Characters and Unicode

Total characters28148
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1511
 
17.0%
1.0 137
 
1.5%

Length

2023-12-31T13:30:33.949486image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:34.141096image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.2%
. 8879
31.5%
1 1648
 
5.9%
- 1511
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
63.1%
Other Punctuation 8879
31.5%
Dash Punctuation 1511
 
5.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1511
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28148
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.2%
. 8879
31.5%
1 1648
 
5.9%
- 1511
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28148
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.2%
. 8879
31.5%
1 1648
 
5.9%
- 1511
 
5.4%

hg_product_76
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.0 KiB
0.0
7231 
-1.0
1409 
1.0
 
239

Length

Max length4
Median length3
Mean length3.158689
Min length3

Characters and Unicode

Total characters28046
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1409
 
15.9%
1.0 239
 
2.7%

Length

2023-12-31T13:30:34.333421image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:34.513617image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.4%
. 8879
31.7%
1 1648
 
5.9%
- 1409
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
63.3%
Other Punctuation 8879
31.7%
Dash Punctuation 1409
 
5.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1409
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28046
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.4%
. 8879
31.7%
1 1648
 
5.9%
- 1409
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28046
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.4%
. 8879
31.7%
1 1648
 
5.9%
- 1409
 
5.0%

hg_product_77
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size590.9 KiB
0.0
7231 
-1.0
1358 
1.0
 
290

Length

Max length4
Median length3
Mean length3.1529452
Min length3

Characters and Unicode

Total characters27995
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1358
 
15.3%
1.0 290
 
3.3%

Length

2023-12-31T13:30:34.704023image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:35.101972image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.5%
. 8879
31.7%
1 1648
 
5.9%
- 1358
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
63.4%
Other Punctuation 8879
31.7%
Dash Punctuation 1358
 
4.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1358
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27995
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.5%
. 8879
31.7%
1 1648
 
5.9%
- 1358
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27995
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.5%
. 8879
31.7%
1 1648
 
5.9%
- 1358
 
4.9%

hg_product_78
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.1 KiB
0.0
7231 
-1.0
1545 
1.0
 
103

Length

Max length4
Median length3
Mean length3.1740061
Min length3

Characters and Unicode

Total characters28182
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1545
 
17.4%
1.0 103
 
1.2%

Length

2023-12-31T13:30:35.287303image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:35.462566image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.2%
. 8879
31.5%
1 1648
 
5.8%
- 1545
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
63.0%
Other Punctuation 8879
31.5%
Dash Punctuation 1545
 
5.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1545
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28182
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.2%
. 8879
31.5%
1 1648
 
5.8%
- 1545
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28182
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.2%
. 8879
31.5%
1 1648
 
5.8%
- 1545
 
5.5%

hg_product_79
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.0 KiB
0.0
7231 
-1.0
1451 
1.0
 
197

Length

Max length4
Median length3
Mean length3.1634193
Min length3

Characters and Unicode

Total characters28088
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1451
 
16.3%
1.0 197
 
2.2%

Length

2023-12-31T13:30:35.650117image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:35.825564image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.4%
. 8879
31.6%
1 1648
 
5.9%
- 1451
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
63.2%
Other Punctuation 8879
31.6%
Dash Punctuation 1451
 
5.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28088
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.4%
. 8879
31.6%
1 1648
 
5.9%
- 1451
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28088
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.4%
. 8879
31.6%
1 1648
 
5.9%
- 1451
 
5.2%

hg_product_80
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.1 KiB
0.0
7231 
-1.0
1551 
1.0
 
97

Length

Max length4
Median length3
Mean length3.1746818
Min length3

Characters and Unicode

Total characters28188
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1551
 
17.5%
1.0 97
 
1.1%

Length

2023-12-31T13:30:36.013525image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:36.187536image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.2%
. 8879
31.5%
1 1648
 
5.8%
- 1551
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
63.0%
Other Punctuation 8879
31.5%
Dash Punctuation 1551
 
5.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1551
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28188
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.2%
. 8879
31.5%
1 1648
 
5.8%
- 1551
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28188
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.2%
. 8879
31.5%
1 1648
 
5.8%
- 1551
 
5.5%

hg_product_81
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.1 KiB
0.0
7231 
-1.0
1478 
1.0
 
170

Length

Max length4
Median length3
Mean length3.1664602
Min length3

Characters and Unicode

Total characters28115
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1478
 
16.6%
1.0 170
 
1.9%

Length

2023-12-31T13:30:36.376784image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:36.552078image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.3%
. 8879
31.6%
1 1648
 
5.9%
- 1478
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
63.2%
Other Punctuation 8879
31.6%
Dash Punctuation 1478
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1478
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28115
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.3%
. 8879
31.6%
1 1648
 
5.9%
- 1478
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28115
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.3%
. 8879
31.6%
1 1648
 
5.9%
- 1478
 
5.3%

hg_product_82
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1647 
1.0
 
1

Length

Max length4
Median length3
Mean length3.1854939
Min length3

Characters and Unicode

Total characters28284
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1647
 
18.5%
1.0 1
 
< 0.1%

Length

2023-12-31T13:30:36.743532image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:36.917554image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1647
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.8%
Other Punctuation 8879
31.4%
Dash Punctuation 1647
 
5.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1647
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28284
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1647
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28284
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1647
 
5.8%

hg_product_83
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size590.4 KiB
0.0
7231 
-1.0
846 
1.0
802 

Length

Max length4
Median length3
Mean length3.095281
Min length3

Characters and Unicode

Total characters27483
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 846
 
9.5%
1.0 802
 
9.0%

Length

2023-12-31T13:30:37.098836image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:37.266886image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
58.6%
. 8879
32.3%
1 1648
 
6.0%
- 846
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
64.6%
Other Punctuation 8879
32.3%
Dash Punctuation 846
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 846
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27483
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
58.6%
. 8879
32.3%
1 1648
 
6.0%
- 846
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27483
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
58.6%
. 8879
32.3%
1 1648
 
6.0%
- 846
 
3.1%

hg_product_84
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1580 
1.0
 
68

Length

Max length4
Median length3
Mean length3.177948
Min length3

Characters and Unicode

Total characters28217
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1580
 
17.8%
1.0 68
 
0.8%

Length

2023-12-31T13:30:37.458314image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:37.633022image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.1%
. 8879
31.5%
1 1648
 
5.8%
- 1580
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.9%
Other Punctuation 8879
31.5%
Dash Punctuation 1580
 
5.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1580
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28217
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.1%
. 8879
31.5%
1 1648
 
5.8%
- 1580
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28217
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.1%
. 8879
31.5%
1 1648
 
5.8%
- 1580
 
5.6%

hg_product_85
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1646 
1.0
 
2

Length

Max length4
Median length3
Mean length3.1853812
Min length3

Characters and Unicode

Total characters28283
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1646
 
18.5%
1.0 2
 
< 0.1%

Length

2023-12-31T13:30:37.822147image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:37.997280image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1646
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.8%
Other Punctuation 8879
31.4%
Dash Punctuation 1646
 
5.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1646
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28283
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1646
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28283
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1646
 
5.8%

hg_product_86
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1599 
1.0
 
49

Length

Max length4
Median length3
Mean length3.1800878
Min length3

Characters and Unicode

Total characters28236
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1599
 
18.0%
1.0 49
 
0.6%

Length

2023-12-31T13:30:38.182771image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:38.359631image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.1%
. 8879
31.4%
1 1648
 
5.8%
- 1599
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.9%
Other Punctuation 8879
31.4%
Dash Punctuation 1599
 
5.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1599
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28236
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.1%
. 8879
31.4%
1 1648
 
5.8%
- 1599
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28236
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.1%
. 8879
31.4%
1 1648
 
5.8%
- 1599
 
5.7%

hg_product_87
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size590.3 KiB
0.0
7231 
1.0
1002 
-1.0
 
646

Length

Max length4
Median length3
Mean length3.0727559
Min length3

Characters and Unicode

Total characters27283
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1002
 
11.3%
-1.0 646
 
7.3%

Length

2023-12-31T13:30:38.540380image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:38.712605image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
59.0%
. 8879
32.5%
1 1648
 
6.0%
- 646
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
65.1%
Other Punctuation 8879
32.5%
Dash Punctuation 646
 
2.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 646
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27283
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
59.0%
. 8879
32.5%
1 1648
 
6.0%
- 646
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27283
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
59.0%
. 8879
32.5%
1 1648
 
6.0%
- 646
 
2.4%

hg_product_88
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size590.7 KiB
0.0
7231 
-1.0
1107 
1.0
 
541

Length

Max length4
Median length3
Mean length3.1246762
Min length3

Characters and Unicode

Total characters27744
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1107
 
12.5%
1.0 541
 
6.1%

Length

2023-12-31T13:30:38.894116image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:39.070793image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
58.1%
. 8879
32.0%
1 1648
 
5.9%
- 1107
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
64.0%
Other Punctuation 8879
32.0%
Dash Punctuation 1107
 
4.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1107
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27744
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
58.1%
. 8879
32.0%
1 1648
 
5.9%
- 1107
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27744
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
58.1%
. 8879
32.0%
1 1648
 
5.9%
- 1107
 
4.0%

hg_product_89
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.1 KiB
0.0
7231 
-1.0
1523 
1.0
 
125

Length

Max length4
Median length3
Mean length3.1715283
Min length3

Characters and Unicode

Total characters28160
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1523
 
17.2%
1.0 125
 
1.4%

Length

2023-12-31T13:30:39.263895image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:39.447913image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.2%
. 8879
31.5%
1 1648
 
5.9%
- 1523
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
63.1%
Other Punctuation 8879
31.5%
Dash Punctuation 1523
 
5.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1523
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28160
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.2%
. 8879
31.5%
1 1648
 
5.9%
- 1523
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28160
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.2%
. 8879
31.5%
1 1648
 
5.9%
- 1523
 
5.4%

hg_product_90
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.1 KiB
0.0
7231 
-1.0
1481 
1.0
 
167

Length

Max length4
Median length3
Mean length3.1667981
Min length3

Characters and Unicode

Total characters28118
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1481
 
16.7%
1.0 167
 
1.9%

Length

2023-12-31T13:30:39.642949image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:39.825499image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.3%
. 8879
31.6%
1 1648
 
5.9%
- 1481
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
63.2%
Other Punctuation 8879
31.6%
Dash Punctuation 1481
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1481
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28118
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.3%
. 8879
31.6%
1 1648
 
5.9%
- 1481
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28118
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.3%
. 8879
31.6%
1 1648
 
5.9%
- 1481
 
5.3%

hg_product_91
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1647 
1.0
 
1

Length

Max length4
Median length3
Mean length3.1854939
Min length3

Characters and Unicode

Total characters28284
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1647
 
18.5%
1.0 1
 
< 0.1%

Length

2023-12-31T13:30:40.018407image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:40.193977image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1647
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.8%
Other Punctuation 8879
31.4%
Dash Punctuation 1647
 
5.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1647
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28284
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1647
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28284
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1647
 
5.8%

hg_product_92
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1626 
1.0
 
22

Length

Max length4
Median length3
Mean length3.1831287
Min length3

Characters and Unicode

Total characters28263
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1626
 
18.3%
1.0 22
 
0.2%

Length

2023-12-31T13:30:40.381901image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:40.555150image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1626
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.8%
Other Punctuation 8879
31.4%
Dash Punctuation 1626
 
5.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1626
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28263
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1626
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28263
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1626
 
5.8%

hg_product_93
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1648 

Length

Max length4
Median length3
Mean length3.1856065
Min length3

Characters and Unicode

Total characters28285
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1648
 
18.6%

Length

2023-12-31T13:30:40.744938image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:40.915065image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
- 1648
 
5.8%
1 1648
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.8%
Other Punctuation 8879
31.4%
Dash Punctuation 1648
 
5.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1648
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28285
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
- 1648
 
5.8%
1 1648
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28285
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
- 1648
 
5.8%
1 1648
 
5.8%

hg_product_94
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1646 
1.0
 
2

Length

Max length4
Median length3
Mean length3.1853812
Min length3

Characters and Unicode

Total characters28283
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1646
 
18.5%
1.0 2
 
< 0.1%

Length

2023-12-31T13:30:41.096786image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:41.274345image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1646
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.8%
Other Punctuation 8879
31.4%
Dash Punctuation 1646
 
5.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1646
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28283
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1646
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28283
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1646
 
5.8%

hg_product_95
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.1 KiB
0.0
7231 
-1.0
1557 
1.0
 
91

Length

Max length4
Median length3
Mean length3.1753576
Min length3

Characters and Unicode

Total characters28194
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1557
 
17.5%
1.0 91
 
1.0%

Length

2023-12-31T13:30:41.468417image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:41.652916image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.1%
. 8879
31.5%
1 1648
 
5.8%
- 1557
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
63.0%
Other Punctuation 8879
31.5%
Dash Punctuation 1557
 
5.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1557
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28194
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.1%
. 8879
31.5%
1 1648
 
5.8%
- 1557
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28194
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.1%
. 8879
31.5%
1 1648
 
5.8%
- 1557
 
5.5%

hg_product_96
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.0 KiB
0.0
7231 
-1.0
1434 
1.0
 
214

Length

Max length4
Median length3
Mean length3.1615047
Min length3

Characters and Unicode

Total characters28071
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1434
 
16.2%
1.0 214
 
2.4%

Length

2023-12-31T13:30:41.845400image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:42.027521image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.4%
. 8879
31.6%
1 1648
 
5.9%
- 1434
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
63.3%
Other Punctuation 8879
31.6%
Dash Punctuation 1434
 
5.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1434
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28071
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.4%
. 8879
31.6%
1 1648
 
5.9%
- 1434
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28071
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.4%
. 8879
31.6%
1 1648
 
5.9%
- 1434
 
5.1%

hg_product_97
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1629 
1.0
 
19

Length

Max length4
Median length3
Mean length3.1834666
Min length3

Characters and Unicode

Total characters28266
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1629
 
18.3%
1.0 19
 
0.2%

Length

2023-12-31T13:30:42.215830image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:42.391629image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1629
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.8%
Other Punctuation 8879
31.4%
Dash Punctuation 1629
 
5.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1629
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28266
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1629
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28266
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1629
 
5.8%

hg_product_98
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1619 
1.0
 
29

Length

Max length4
Median length3
Mean length3.1823404
Min length3

Characters and Unicode

Total characters28256
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1619
 
18.2%
1.0 29
 
0.3%

Length

2023-12-31T13:30:42.583555image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:42.768532image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1619
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.8%
Other Punctuation 8879
31.4%
Dash Punctuation 1619
 
5.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1619
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28256
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1619
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28256
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1619
 
5.7%

hg_product_99
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1615 
1.0
 
33

Length

Max length4
Median length3
Mean length3.1818899
Min length3

Characters and Unicode

Total characters28252
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1615
 
18.2%
1.0 33
 
0.4%

Length

2023-12-31T13:30:42.965193image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:43.140789image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1615
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.9%
Other Punctuation 8879
31.4%
Dash Punctuation 1615
 
5.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1615
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28252
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1615
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28252
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1615
 
5.7%

hg_product_100
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1593 
1.0
 
55

Length

Max length4
Median length3
Mean length3.1794121
Min length3

Characters and Unicode

Total characters28230
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1593
 
17.9%
1.0 55
 
0.6%

Length

2023-12-31T13:30:43.335635image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:43.515346image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.1%
. 8879
31.5%
1 1648
 
5.8%
- 1593
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.9%
Other Punctuation 8879
31.5%
Dash Punctuation 1593
 
5.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1593
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28230
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.1%
. 8879
31.5%
1 1648
 
5.8%
- 1593
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28230
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.1%
. 8879
31.5%
1 1648
 
5.8%
- 1593
 
5.6%

hg_product_101
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1591 
1.0
 
57

Length

Max length4
Median length3
Mean length3.1791868
Min length3

Characters and Unicode

Total characters28228
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1591
 
17.9%
1.0 57
 
0.6%

Length

2023-12-31T13:30:43.710237image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:43.889564image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.1%
. 8879
31.5%
1 1648
 
5.8%
- 1591
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.9%
Other Punctuation 8879
31.5%
Dash Punctuation 1591
 
5.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1591
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28228
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.1%
. 8879
31.5%
1 1648
 
5.8%
- 1591
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28228
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.1%
. 8879
31.5%
1 1648
 
5.8%
- 1591
 
5.6%

hg_product_102
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size590.7 KiB
0.0
7231 
-1.0
1077 
1.0
 
571

Length

Max length4
Median length3
Mean length3.1212974
Min length3

Characters and Unicode

Total characters27714
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1077
 
12.1%
1.0 571
 
6.4%

Length

2023-12-31T13:30:44.073671image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:44.248320image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
58.1%
. 8879
32.0%
1 1648
 
5.9%
- 1077
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
64.1%
Other Punctuation 8879
32.0%
Dash Punctuation 1077
 
3.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1077
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27714
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
58.1%
. 8879
32.0%
1 1648
 
5.9%
- 1077
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27714
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
58.1%
. 8879
32.0%
1 1648
 
5.9%
- 1077
 
3.9%

hg_product_103
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.0 KiB
0.0
7231 
-1.0
1462 
1.0
 
186

Length

Max length4
Median length3
Mean length3.1646582
Min length3

Characters and Unicode

Total characters28099
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1462
 
16.5%
1.0 186
 
2.1%

Length

2023-12-31T13:30:44.439730image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:44.619616image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.3%
. 8879
31.6%
1 1648
 
5.9%
- 1462
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
63.2%
Other Punctuation 8879
31.6%
Dash Punctuation 1462
 
5.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1462
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28099
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.3%
. 8879
31.6%
1 1648
 
5.9%
- 1462
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28099
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.3%
. 8879
31.6%
1 1648
 
5.9%
- 1462
 
5.2%

hg_product_104
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1615 
1.0
 
33

Length

Max length4
Median length3
Mean length3.1818899
Min length3

Characters and Unicode

Total characters28252
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1615
 
18.2%
1.0 33
 
0.4%

Length

2023-12-31T13:30:44.808524image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:44.985093image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1615
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.9%
Other Punctuation 8879
31.4%
Dash Punctuation 1615
 
5.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1615
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28252
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1615
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28252
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1615
 
5.7%

hg_product_105
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.1 KiB
0.0
7231 
-1.0
1498 
1.0
 
150

Length

Max length4
Median length3
Mean length3.1687127
Min length3

Characters and Unicode

Total characters28135
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1498
 
16.9%
1.0 150
 
1.7%

Length

2023-12-31T13:30:45.174599image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:45.353235image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.3%
. 8879
31.6%
1 1648
 
5.9%
- 1498
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
63.1%
Other Punctuation 8879
31.6%
Dash Punctuation 1498
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1498
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28135
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.3%
. 8879
31.6%
1 1648
 
5.9%
- 1498
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28135
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.3%
. 8879
31.6%
1 1648
 
5.9%
- 1498
 
5.3%

hg_product_106
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1635 
1.0
 
13

Length

Max length4
Median length3
Mean length3.1841424
Min length3

Characters and Unicode

Total characters28272
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1635
 
18.4%
1.0 13
 
0.1%

Length

2023-12-31T13:30:45.552106image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:45.742326image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1635
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.8%
Other Punctuation 8879
31.4%
Dash Punctuation 1635
 
5.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1635
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28272
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1635
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28272
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1635
 
5.8%

hg_product_107
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1634 
1.0
 
14

Length

Max length4
Median length3
Mean length3.1840297
Min length3

Characters and Unicode

Total characters28271
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1634
 
18.4%
1.0 14
 
0.2%

Length

2023-12-31T13:30:45.957027image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:46.141660image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1634
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.8%
Other Punctuation 8879
31.4%
Dash Punctuation 1634
 
5.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1634
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28271
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1634
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28271
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1634
 
5.8%

hg_product_108
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1648 

Length

Max length4
Median length3
Mean length3.1856065
Min length3

Characters and Unicode

Total characters28285
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1648
 
18.6%

Length

2023-12-31T13:30:46.337480image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:46.515590image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
- 1648
 
5.8%
1 1648
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.8%
Other Punctuation 8879
31.4%
Dash Punctuation 1648
 
5.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1648
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28285
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
- 1648
 
5.8%
1 1648
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28285
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
- 1648
 
5.8%
1 1648
 
5.8%

hg_product_109
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1647 
1.0
 
1

Length

Max length4
Median length3
Mean length3.1854939
Min length3

Characters and Unicode

Total characters28284
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1647
 
18.5%
1.0 1
 
< 0.1%

Length

2023-12-31T13:30:46.702834image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:46.885703image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1647
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.8%
Other Punctuation 8879
31.4%
Dash Punctuation 1647
 
5.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1647
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28284
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1647
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28284
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1647
 
5.8%

hg_product_110
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1638 
1.0
 
10

Length

Max length4
Median length3
Mean length3.1844802
Min length3

Characters and Unicode

Total characters28275
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1638
 
18.4%
1.0 10
 
0.1%

Length

2023-12-31T13:30:47.082175image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:47.267713image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1638
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.8%
Other Punctuation 8879
31.4%
Dash Punctuation 1638
 
5.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1638
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28275
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1638
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28275
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1638
 
5.8%

hg_product_111
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1646 
1.0
 
2

Length

Max length4
Median length3
Mean length3.1853812
Min length3

Characters and Unicode

Total characters28283
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1646
 
18.5%
1.0 2
 
< 0.1%

Length

2023-12-31T13:30:47.464552image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:47.646026image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1646
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.8%
Other Punctuation 8879
31.4%
Dash Punctuation 1646
 
5.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1646
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28283
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1646
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28283
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1646
 
5.8%

hg_product_112
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.1 KiB
0.0
7231 
-1.0
1530 
1.0
 
118

Length

Max length4
Median length3
Mean length3.1723167
Min length3

Characters and Unicode

Total characters28167
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1530
 
17.2%
1.0 118
 
1.3%

Length

2023-12-31T13:30:47.836787image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:48.017337image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.2%
. 8879
31.5%
1 1648
 
5.9%
- 1530
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
63.0%
Other Punctuation 8879
31.5%
Dash Punctuation 1530
 
5.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1530
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28167
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.2%
. 8879
31.5%
1 1648
 
5.9%
- 1530
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28167
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.2%
. 8879
31.5%
1 1648
 
5.9%
- 1530
 
5.4%

hg_product_113
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1648 

Length

Max length4
Median length3
Mean length3.1856065
Min length3

Characters and Unicode

Total characters28285
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1648
 
18.6%

Length

2023-12-31T13:30:48.211348image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:48.384521image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
- 1648
 
5.8%
1 1648
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.8%
Other Punctuation 8879
31.4%
Dash Punctuation 1648
 
5.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1648
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28285
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
- 1648
 
5.8%
1 1648
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28285
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
- 1648
 
5.8%
1 1648
 
5.8%

hg_product_114
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.1 KiB
0.0
7231 
-1.0
1536 
1.0
 
112

Length

Max length4
Median length3
Mean length3.1729925
Min length3

Characters and Unicode

Total characters28173
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1536
 
17.3%
1.0 112
 
1.3%

Length

2023-12-31T13:30:48.571151image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:48.747375image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.2%
. 8879
31.5%
1 1648
 
5.8%
- 1536
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
63.0%
Other Punctuation 8879
31.5%
Dash Punctuation 1536
 
5.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1536
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28173
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.2%
. 8879
31.5%
1 1648
 
5.8%
- 1536
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28173
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.2%
. 8879
31.5%
1 1648
 
5.8%
- 1536
 
5.5%

hg_product_115
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1642 
1.0
 
6

Length

Max length4
Median length3
Mean length3.1849307
Min length3

Characters and Unicode

Total characters28279
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1642
 
18.5%
1.0 6
 
0.1%

Length

2023-12-31T13:30:49.186581image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:49.401701image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1642
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.8%
Other Punctuation 8879
31.4%
Dash Punctuation 1642
 
5.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1642
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28279
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1642
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28279
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1642
 
5.8%

hg_product_116
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1621 
1.0
 
27

Length

Max length4
Median length3
Mean length3.1825656
Min length3

Characters and Unicode

Total characters28258
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1621
 
18.3%
1.0 27
 
0.3%

Length

2023-12-31T13:30:49.595100image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:49.771533image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1621
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.8%
Other Punctuation 8879
31.4%
Dash Punctuation 1621
 
5.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1621
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28258
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1621
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28258
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1621
 
5.7%

hg_product_117
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1636 
1.0
 
12

Length

Max length4
Median length3
Mean length3.184255
Min length3

Characters and Unicode

Total characters28273
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1636
 
18.4%
1.0 12
 
0.1%

Length

2023-12-31T13:30:49.966631image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:50.145662image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1636
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.8%
Other Punctuation 8879
31.4%
Dash Punctuation 1636
 
5.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1636
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28273
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1636
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28273
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1636
 
5.8%

hg_product_118
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1647 
1.0
 
1

Length

Max length4
Median length3
Mean length3.1854939
Min length3

Characters and Unicode

Total characters28284
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1647
 
18.5%
1.0 1
 
< 0.1%

Length

2023-12-31T13:30:50.337404image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:50.516504image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1647
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.8%
Other Punctuation 8879
31.4%
Dash Punctuation 1647
 
5.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1647
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28284
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1647
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28284
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1647
 
5.8%

hg_product_119
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.1 KiB
0.0
7231 
-1.0
1503 
1.0
 
145

Length

Max length4
Median length3
Mean length3.1692758
Min length3

Characters and Unicode

Total characters28140
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1503
 
16.9%
1.0 145
 
1.6%

Length

2023-12-31T13:30:50.708068image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:50.889700image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.2%
. 8879
31.6%
1 1648
 
5.9%
- 1503
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
63.1%
Other Punctuation 8879
31.6%
Dash Punctuation 1503
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1503
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28140
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.2%
. 8879
31.6%
1 1648
 
5.9%
- 1503
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28140
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.2%
. 8879
31.6%
1 1648
 
5.9%
- 1503
 
5.3%

hg_product_120
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1616 
1.0
 
32

Length

Max length4
Median length3
Mean length3.1820025
Min length3

Characters and Unicode

Total characters28253
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1616
 
18.2%
1.0 32
 
0.4%

Length

2023-12-31T13:30:51.079218image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:51.254946image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1616
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.9%
Other Punctuation 8879
31.4%
Dash Punctuation 1616
 
5.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1616
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28253
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1616
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28253
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1616
 
5.7%

hg_product_121
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1644 
1.0
 
4

Length

Max length4
Median length3
Mean length3.185156
Min length3

Characters and Unicode

Total characters28281
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1644
 
18.5%
1.0 4
 
< 0.1%

Length

2023-12-31T13:30:51.440527image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:51.618137image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1644
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.8%
Other Punctuation 8879
31.4%
Dash Punctuation 1644
 
5.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1644
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28281
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1644
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28281
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1644
 
5.8%

hg_product_122
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1648 

Length

Max length4
Median length3
Mean length3.1856065
Min length3

Characters and Unicode

Total characters28285
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1648
 
18.6%

Length

2023-12-31T13:30:51.806171image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:51.976304image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
- 1648
 
5.8%
1 1648
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.8%
Other Punctuation 8879
31.4%
Dash Punctuation 1648
 
5.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1648
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28285
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
- 1648
 
5.8%
1 1648
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28285
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
- 1648
 
5.8%
1 1648
 
5.8%

hg_product_123
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.1 KiB
0.0
7231 
-1.0
1534 
1.0
 
114

Length

Max length4
Median length3
Mean length3.1727672
Min length3

Characters and Unicode

Total characters28171
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1534
 
17.3%
1.0 114
 
1.3%

Length

2023-12-31T13:30:52.155487image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:52.329064image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.2%
. 8879
31.5%
1 1648
 
5.8%
- 1534
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
63.0%
Other Punctuation 8879
31.5%
Dash Punctuation 1534
 
5.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1534
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28171
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.2%
. 8879
31.5%
1 1648
 
5.8%
- 1534
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28171
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.2%
. 8879
31.5%
1 1648
 
5.8%
- 1534
 
5.4%

hg_product_124
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size590.7 KiB
0.0
7231 
-1.0
1126 
1.0
 
522

Length

Max length4
Median length3
Mean length3.1268161
Min length3

Characters and Unicode

Total characters27763
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1126
 
12.7%
1.0 522
 
5.9%

Length

2023-12-31T13:30:52.507080image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:52.672326image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
58.0%
. 8879
32.0%
1 1648
 
5.9%
- 1126
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
64.0%
Other Punctuation 8879
32.0%
Dash Punctuation 1126
 
4.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1126
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27763
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
58.0%
. 8879
32.0%
1 1648
 
5.9%
- 1126
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27763
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
58.0%
. 8879
32.0%
1 1648
 
5.9%
- 1126
 
4.1%

hg_product_125
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size590.3 KiB
0.0
7231 
1.0
963 
-1.0
 
685

Length

Max length4
Median length3
Mean length3.0771483
Min length3

Characters and Unicode

Total characters27322
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
1.0 963
 
10.8%
-1.0 685
 
7.7%

Length

2023-12-31T13:30:52.853027image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:53.021360image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
59.0%
. 8879
32.5%
1 1648
 
6.0%
- 685
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
65.0%
Other Punctuation 8879
32.5%
Dash Punctuation 685
 
2.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 685
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27322
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
59.0%
. 8879
32.5%
1 1648
 
6.0%
- 685
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27322
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
59.0%
. 8879
32.5%
1 1648
 
6.0%
- 685
 
2.5%

hg_product_126
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1598 
1.0
 
50

Length

Max length4
Median length3
Mean length3.1799752
Min length3

Characters and Unicode

Total characters28235
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1598
 
18.0%
1.0 50
 
0.6%

Length

2023-12-31T13:30:53.208793image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:53.379817image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.1%
. 8879
31.4%
1 1648
 
5.8%
- 1598
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.9%
Other Punctuation 8879
31.4%
Dash Punctuation 1598
 
5.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1598
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28235
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.1%
. 8879
31.4%
1 1648
 
5.8%
- 1598
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28235
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.1%
. 8879
31.4%
1 1648
 
5.8%
- 1598
 
5.7%

hg_product_127
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1601 
1.0
 
47

Length

Max length4
Median length3
Mean length3.1803131
Min length3

Characters and Unicode

Total characters28238
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1601
 
18.0%
1.0 47
 
0.5%

Length

2023-12-31T13:30:53.566696image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:53.740479image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.1%
. 8879
31.4%
1 1648
 
5.8%
- 1601
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.9%
Other Punctuation 8879
31.4%
Dash Punctuation 1601
 
5.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1601
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28238
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.1%
. 8879
31.4%
1 1648
 
5.8%
- 1601
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28238
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.1%
. 8879
31.4%
1 1648
 
5.8%
- 1601
 
5.7%

hg_product_128
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1644 
1.0
 
4

Length

Max length4
Median length3
Mean length3.185156
Min length3

Characters and Unicode

Total characters28281
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1644
 
18.5%
1.0 4
 
< 0.1%

Length

2023-12-31T13:30:53.929880image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:54.109885image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1644
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.8%
Other Punctuation 8879
31.4%
Dash Punctuation 1644
 
5.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1644
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28281
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1644
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28281
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1644
 
5.8%

hg_product_129
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size590.0 KiB
0.0
7231 
1.0
1236 
-1.0
 
412

Length

Max length4
Median length3
Mean length3.0464016
Min length3

Characters and Unicode

Total characters27049
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1236
 
13.9%
-1.0 412
 
4.6%

Length

2023-12-31T13:30:54.288747image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:54.457558image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
59.6%
. 8879
32.8%
1 1648
 
6.1%
- 412
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
65.7%
Other Punctuation 8879
32.8%
Dash Punctuation 412
 
1.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 412
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27049
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
59.6%
. 8879
32.8%
1 1648
 
6.1%
- 412
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27049
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
59.6%
. 8879
32.8%
1 1648
 
6.1%
- 412
 
1.5%

hg_product_130
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1637 
1.0
 
11

Length

Max length4
Median length3
Mean length3.1843676
Min length3

Characters and Unicode

Total characters28274
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1637
 
18.4%
1.0 11
 
0.1%

Length

2023-12-31T13:30:54.644623image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:54.824065image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1637
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.8%
Other Punctuation 8879
31.4%
Dash Punctuation 1637
 
5.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1637
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28274
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1637
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28274
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1637
 
5.8%

hg_product_131
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1629 
1.0
 
19

Length

Max length4
Median length3
Mean length3.1834666
Min length3

Characters and Unicode

Total characters28266
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1629
 
18.3%
1.0 19
 
0.2%

Length

2023-12-31T13:30:55.013762image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:55.188163image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1629
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.8%
Other Punctuation 8879
31.4%
Dash Punctuation 1629
 
5.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1629
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28266
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1629
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28266
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1629
 
5.8%

hg_product_132
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.0 KiB
0.0
7231 
-1.0
1392 
1.0
 
256

Length

Max length4
Median length3
Mean length3.1567744
Min length3

Characters and Unicode

Total characters28029
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1392
 
15.7%
1.0 256
 
2.9%

Length

2023-12-31T13:30:55.381069image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:55.555304image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.5%
. 8879
31.7%
1 1648
 
5.9%
- 1392
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
63.4%
Other Punctuation 8879
31.7%
Dash Punctuation 1392
 
5.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1392
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28029
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.5%
. 8879
31.7%
1 1648
 
5.9%
- 1392
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28029
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.5%
. 8879
31.7%
1 1648
 
5.9%
- 1392
 
5.0%

hg_product_133
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1635 
1.0
 
13

Length

Max length4
Median length3
Mean length3.1841424
Min length3

Characters and Unicode

Total characters28272
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1635
 
18.4%
1.0 13
 
0.1%

Length

2023-12-31T13:30:55.748954image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:55.923646image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1635
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.8%
Other Punctuation 8879
31.4%
Dash Punctuation 1635
 
5.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1635
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28272
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1635
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28272
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1635
 
5.8%

hg_product_134
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1636 
1.0
 
12

Length

Max length4
Median length3
Mean length3.184255
Min length3

Characters and Unicode

Total characters28273
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1636
 
18.4%
1.0 12
 
0.1%

Length

2023-12-31T13:30:56.110672image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:56.286190image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1636
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.8%
Other Punctuation 8879
31.4%
Dash Punctuation 1636
 
5.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1636
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28273
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1636
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28273
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1636
 
5.8%

hg_product_135
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1647 
1.0
 
1

Length

Max length4
Median length3
Mean length3.1854939
Min length3

Characters and Unicode

Total characters28284
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1647
 
18.5%
1.0 1
 
< 0.1%

Length

2023-12-31T13:30:56.474649image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:56.647987image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1647
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.8%
Other Punctuation 8879
31.4%
Dash Punctuation 1647
 
5.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1647
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28284
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1647
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28284
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1647
 
5.8%

hg_product_136
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1646 
1.0
 
2

Length

Max length4
Median length3
Mean length3.1853812
Min length3

Characters and Unicode

Total characters28283
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1646
 
18.5%
1.0 2
 
< 0.1%

Length

2023-12-31T13:30:56.833736image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:57.007714image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1646
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.8%
Other Punctuation 8879
31.4%
Dash Punctuation 1646
 
5.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1646
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28283
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1646
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28283
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1646
 
5.8%

hg_product_137
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1605 
1.0
 
43

Length

Max length4
Median length3
Mean length3.1807636
Min length3

Characters and Unicode

Total characters28242
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1605
 
18.1%
1.0 43
 
0.5%

Length

2023-12-31T13:30:57.192182image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:57.364319image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1605
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.9%
Other Punctuation 8879
31.4%
Dash Punctuation 1605
 
5.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1605
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28242
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1605
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28242
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1605
 
5.7%

hg_product_138
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1624 
1.0
 
24

Length

Max length4
Median length3
Mean length3.1829035
Min length3

Characters and Unicode

Total characters28261
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1624
 
18.3%
1.0 24
 
0.3%

Length

2023-12-31T13:30:57.548852image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:57.721926image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1624
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.8%
Other Punctuation 8879
31.4%
Dash Punctuation 1624
 
5.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1624
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28261
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1624
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28261
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1624
 
5.7%

hg_product_139
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size590.7 KiB
0.0
7231 
-1.0
1124 
1.0
 
524

Length

Max length4
Median length3
Mean length3.1265908
Min length3

Characters and Unicode

Total characters27761
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1124
 
12.7%
1.0 524
 
5.9%

Length

2023-12-31T13:30:57.899640image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:58.063183image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
58.0%
. 8879
32.0%
1 1648
 
5.9%
- 1124
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
64.0%
Other Punctuation 8879
32.0%
Dash Punctuation 1124
 
4.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1124
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27761
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
58.0%
. 8879
32.0%
1 1648
 
5.9%
- 1124
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27761
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
58.0%
. 8879
32.0%
1 1648
 
5.9%
- 1124
 
4.0%

hg_product_140
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size590.4 KiB
0.0
7231 
1.0
874 
-1.0
774 

Length

Max length4
Median length3
Mean length3.087172
Min length3

Characters and Unicode

Total characters27411
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
1.0 874
 
9.8%
-1.0 774
 
8.7%

Length

2023-12-31T13:30:58.240825image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:58.409964image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
58.8%
. 8879
32.4%
1 1648
 
6.0%
- 774
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
64.8%
Other Punctuation 8879
32.4%
Dash Punctuation 774
 
2.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 774
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27411
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
58.8%
. 8879
32.4%
1 1648
 
6.0%
- 774
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27411
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
58.8%
. 8879
32.4%
1 1648
 
6.0%
- 774
 
2.8%

hg_product_141
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1574 
1.0
 
74

Length

Max length4
Median length3
Mean length3.1772722
Min length3

Characters and Unicode

Total characters28211
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1574
 
17.7%
1.0 74
 
0.8%

Length

2023-12-31T13:30:58.598730image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:58.771011image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.1%
. 8879
31.5%
1 1648
 
5.8%
- 1574
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.9%
Other Punctuation 8879
31.5%
Dash Punctuation 1574
 
5.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1574
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28211
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.1%
. 8879
31.5%
1 1648
 
5.8%
- 1574
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28211
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.1%
. 8879
31.5%
1 1648
 
5.8%
- 1574
 
5.6%

hg_product_142
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.1 KiB
0.0
7231 
-1.0
1565 
1.0
 
83

Length

Max length4
Median length3
Mean length3.1762586
Min length3

Characters and Unicode

Total characters28202
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1565
 
17.6%
1.0 83
 
0.9%

Length

2023-12-31T13:30:58.963793image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:59.135860image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.1%
. 8879
31.5%
1 1648
 
5.8%
- 1565
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
63.0%
Other Punctuation 8879
31.5%
Dash Punctuation 1565
 
5.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1565
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28202
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.1%
. 8879
31.5%
1 1648
 
5.8%
- 1565
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28202
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.1%
. 8879
31.5%
1 1648
 
5.8%
- 1565
 
5.5%

hg_product_143
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size590.9 KiB
0.0
7231 
-1.0
1323 
1.0
 
325

Length

Max length4
Median length3
Mean length3.1490033
Min length3

Characters and Unicode

Total characters27960
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1323
 
14.9%
1.0 325
 
3.7%

Length

2023-12-31T13:30:59.319989image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:59.493593image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.6%
. 8879
31.8%
1 1648
 
5.9%
- 1323
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
63.5%
Other Punctuation 8879
31.8%
Dash Punctuation 1323
 
4.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1323
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27960
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.6%
. 8879
31.8%
1 1648
 
5.9%
- 1323
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27960
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.6%
. 8879
31.8%
1 1648
 
5.9%
- 1323
 
4.7%

hg_product_144
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size590.3 KiB
0.0
7231 
1.0
988 
-1.0
 
660

Length

Max length4
Median length3
Mean length3.0743327
Min length3

Characters and Unicode

Total characters27297
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
1.0 988
 
11.1%
-1.0 660
 
7.4%

Length

2023-12-31T13:30:59.668215image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:30:59.835740image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
59.0%
. 8879
32.5%
1 1648
 
6.0%
- 660
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
65.1%
Other Punctuation 8879
32.5%
Dash Punctuation 660
 
2.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 660
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27297
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
59.0%
. 8879
32.5%
1 1648
 
6.0%
- 660
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27297
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
59.0%
. 8879
32.5%
1 1648
 
6.0%
- 660
 
2.4%

hg_product_145
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size590.6 KiB
0.0
7231 
-1.0
1027 
1.0
 
621

Length

Max length4
Median length3
Mean length3.1156662
Min length3

Characters and Unicode

Total characters27664
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1027
 
11.6%
1.0 621
 
7.0%

Length

2023-12-31T13:31:00.018902image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:31:00.189156image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
58.2%
. 8879
32.1%
1 1648
 
6.0%
- 1027
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
64.2%
Other Punctuation 8879
32.1%
Dash Punctuation 1027
 
3.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1027
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27664
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
58.2%
. 8879
32.1%
1 1648
 
6.0%
- 1027
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27664
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
58.2%
. 8879
32.1%
1 1648
 
6.0%
- 1027
 
3.7%

hg_product_146
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1604 
1.0
 
44

Length

Max length4
Median length3
Mean length3.180651
Min length3

Characters and Unicode

Total characters28241
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1604
 
18.1%
1.0 44
 
0.5%

Length

2023-12-31T13:31:00.371934image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:31:00.544417image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1604
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.9%
Other Punctuation 8879
31.4%
Dash Punctuation 1604
 
5.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1604
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28241
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1604
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28241
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1604
 
5.7%

hg_product_147
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1591 
1.0
 
57

Length

Max length4
Median length3
Mean length3.1791868
Min length3

Characters and Unicode

Total characters28228
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1591
 
17.9%
1.0 57
 
0.6%

Length

2023-12-31T13:31:00.757525image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:31:00.945301image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.1%
. 8879
31.5%
1 1648
 
5.8%
- 1591
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.9%
Other Punctuation 8879
31.5%
Dash Punctuation 1591
 
5.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1591
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28228
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.1%
. 8879
31.5%
1 1648
 
5.8%
- 1591
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28228
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.1%
. 8879
31.5%
1 1648
 
5.8%
- 1591
 
5.6%

hg_product_148
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size591.2 KiB
0.0
7231 
-1.0
1639 
1.0
 
9

Length

Max length4
Median length3
Mean length3.1845929
Min length3

Characters and Unicode

Total characters28276
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7231
81.4%
-1.0 1639
 
18.5%
1.0 9
 
0.1%

Length

2023-12-31T13:31:01.150966image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:31:01.328098image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7231
81.4%
1.0 1648
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1639
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
62.8%
Other Punctuation 8879
31.4%
Dash Punctuation 1639
 
5.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16110
90.7%
1 1648
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1639
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28276
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1639
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28276
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16110
57.0%
. 8879
31.4%
1 1648
 
5.8%
- 1639
 
5.8%

country_top
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size585.7 KiB
US
4983 
Other
1469 
GB
710 
CA
 
341
AU
 
332
Other values (6)
1044 

Length

Max length7
Median length2
Mean length2.5481473
Min length2

Characters and Unicode

Total characters22625
Distinct characters23
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNL
2nd rowUS
3rd rowGB
4th rowGB
5th rowOther

Common Values

ValueCountFrequency (%)
US 4983
56.1%
Other 1469
 
16.5%
GB 710
 
8.0%
CA 341
 
3.8%
AU 332
 
3.7%
DE 322
 
3.6%
FR 244
 
2.7%
NL 178
 
2.0%
IN 108
 
1.2%
BR 100
 
1.1%

Length

2023-12-31T13:31:01.528354image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
us 4983
56.1%
other 1469
 
16.5%
gb 710
 
8.0%
ca 341
 
3.8%
au 332
 
3.7%
de 322
 
3.6%
fr 244
 
2.7%
nl 178
 
2.0%
in 108
 
1.2%
br 100
 
1.1%

Most occurring characters

ValueCountFrequency (%)
U 5315
23.5%
S 4983
22.0%
O 1469
 
6.5%
t 1469
 
6.5%
h 1469
 
6.5%
e 1469
 
6.5%
r 1469
 
6.5%
B 810
 
3.6%
G 710
 
3.1%
A 673
 
3.0%
Other values (13) 2789
12.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 16197
71.6%
Lowercase Letter 6428
 
28.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 5315
32.8%
S 4983
30.8%
O 1469
 
9.1%
B 810
 
5.0%
G 710
 
4.4%
A 673
 
4.2%
R 344
 
2.1%
C 341
 
2.1%
D 322
 
2.0%
E 322
 
2.0%
Other values (5) 908
 
5.6%
Lowercase Letter
ValueCountFrequency (%)
t 1469
22.9%
h 1469
22.9%
e 1469
22.9%
r 1469
22.9%
i 184
 
2.9%
s 184
 
2.9%
n 92
 
1.4%
g 92
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 22625
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 5315
23.5%
S 4983
22.0%
O 1469
 
6.5%
t 1469
 
6.5%
h 1469
 
6.5%
e 1469
 
6.5%
r 1469
 
6.5%
B 810
 
3.6%
G 710
 
3.1%
A 673
 
3.0%
Other values (13) 2789
12.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22625
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 5315
23.5%
S 4983
22.0%
O 1469
 
6.5%
t 1469
 
6.5%
h 1469
 
6.5%
e 1469
 
6.5%
r 1469
 
6.5%
B 810
 
3.6%
G 710
 
3.1%
A 673
 
3.0%
Other values (13) 2789
12.3%

state_top
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size604.1 KiB
Missing
3965 
CA
1472 
Other
1310 
NY
781 
TX
 
305
Other values (6)
1046 

Length

Max length7
Median length5
Mean length4.6754139
Min length2

Characters and Unicode

Total characters41513
Distinct characters21
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMissing
2nd rowMA
3rd rowMissing
4th rowMissing
5th rowMissing

Common Values

ValueCountFrequency (%)
Missing 3965
44.7%
CA 1472
 
16.6%
Other 1310
 
14.8%
NY 781
 
8.8%
TX 305
 
3.4%
MA 255
 
2.9%
IL 200
 
2.3%
FL 178
 
2.0%
WA 153
 
1.7%
CO 147
 
1.7%

Length

2023-12-31T13:31:01.729555image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
missing 3965
44.7%
ca 1472
 
16.6%
other 1310
 
14.8%
ny 781
 
8.8%
tx 305
 
3.4%
ma 255
 
2.9%
il 200
 
2.3%
fl 178
 
2.0%
wa 153
 
1.7%
co 147
 
1.7%

Most occurring characters

ValueCountFrequency (%)
i 7930
19.1%
s 7930
19.1%
M 4220
10.2%
n 3965
9.6%
g 3965
9.6%
A 1993
 
4.8%
C 1619
 
3.9%
O 1457
 
3.5%
r 1310
 
3.2%
e 1310
 
3.2%
Other values (11) 5814
14.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 29030
69.9%
Uppercase Letter 12483
30.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 4220
33.8%
A 1993
16.0%
C 1619
 
13.0%
O 1457
 
11.7%
N 781
 
6.3%
Y 781
 
6.3%
L 378
 
3.0%
T 305
 
2.4%
X 305
 
2.4%
I 200
 
1.6%
Other values (3) 444
 
3.6%
Lowercase Letter
ValueCountFrequency (%)
i 7930
27.3%
s 7930
27.3%
n 3965
13.7%
g 3965
13.7%
r 1310
 
4.5%
e 1310
 
4.5%
h 1310
 
4.5%
t 1310
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 41513
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 7930
19.1%
s 7930
19.1%
M 4220
10.2%
n 3965
9.6%
g 3965
9.6%
A 1993
 
4.8%
C 1619
 
3.9%
O 1457
 
3.5%
r 1310
 
3.2%
e 1310
 
3.2%
Other values (11) 5814
14.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 41513
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 7930
19.1%
s 7930
19.1%
M 4220
10.2%
n 3965
9.6%
g 3965
9.6%
A 1993
 
4.8%
C 1619
 
3.9%
O 1457
 
3.5%
r 1310
 
3.2%
e 1310
 
3.2%
Other values (11) 5814
14.0%

country_top_AU
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.0 KiB
False
8547 
True
 
332
ValueCountFrequency (%)
False 8547
96.3%
True 332
 
3.7%
2023-12-31T13:31:01.905569image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

country_top_BR
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.0 KiB
False
8779 
True
 
100
ValueCountFrequency (%)
False 8779
98.9%
True 100
 
1.1%
2023-12-31T13:31:02.059806image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

country_top_CA
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.0 KiB
False
8538 
True
 
341
ValueCountFrequency (%)
False 8538
96.2%
True 341
 
3.8%
2023-12-31T13:31:02.215152image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

country_top_DE
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.0 KiB
False
8557 
True
 
322
ValueCountFrequency (%)
False 8557
96.4%
True 322
 
3.6%
2023-12-31T13:31:02.369711image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

country_top_FR
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.0 KiB
False
8635 
True
 
244
ValueCountFrequency (%)
False 8635
97.3%
True 244
 
2.7%
2023-12-31T13:31:02.522203image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

country_top_GB
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.0 KiB
False
8169 
True
 
710
ValueCountFrequency (%)
False 8169
92.0%
True 710
 
8.0%
2023-12-31T13:31:02.677684image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

country_top_IN
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.0 KiB
False
8771 
True
 
108
ValueCountFrequency (%)
False 8771
98.8%
True 108
 
1.2%
2023-12-31T13:31:03.107611image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

country_top_Missing
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.0 KiB
False
8787 
True
 
92
ValueCountFrequency (%)
False 8787
99.0%
True 92
 
1.0%
2023-12-31T13:31:03.262366image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

country_top_NL
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.0 KiB
False
8701 
True
 
178
ValueCountFrequency (%)
False 8701
98.0%
True 178
 
2.0%
2023-12-31T13:31:03.450466image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

country_top_Other
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.0 KiB
False
7410 
True
1469 
ValueCountFrequency (%)
False 7410
83.5%
True 1469
 
16.5%
2023-12-31T13:31:03.606548image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

country_top_US
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.0 KiB
True
4983 
False
3896 
ValueCountFrequency (%)
True 4983
56.1%
False 3896
43.9%
2023-12-31T13:31:03.767211image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

state_top_CA
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.0 KiB
False
7407 
True
1472 
ValueCountFrequency (%)
False 7407
83.4%
True 1472
 
16.6%
2023-12-31T13:31:03.925701image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

state_top_CO
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.0 KiB
False
8732 
True
 
147
ValueCountFrequency (%)
False 8732
98.3%
True 147
 
1.7%
2023-12-31T13:31:04.081867image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

state_top_FL
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.0 KiB
False
8701 
True
 
178
ValueCountFrequency (%)
False 8701
98.0%
True 178
 
2.0%
2023-12-31T13:31:04.234516image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

state_top_GA
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.0 KiB
False
8766 
True
 
113
ValueCountFrequency (%)
False 8766
98.7%
True 113
 
1.3%
2023-12-31T13:31:04.380706image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

state_top_IL
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.0 KiB
False
8679 
True
 
200
ValueCountFrequency (%)
False 8679
97.7%
True 200
 
2.3%
2023-12-31T13:31:04.529250image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

state_top_MA
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.0 KiB
False
8624 
True
 
255
ValueCountFrequency (%)
False 8624
97.1%
True 255
 
2.9%
2023-12-31T13:31:04.676343image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

state_top_Missing
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.0 KiB
False
4914 
True
3965 
ValueCountFrequency (%)
False 4914
55.3%
True 3965
44.7%
2023-12-31T13:31:04.829788image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

state_top_NY
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.0 KiB
False
8098 
True
 
781
ValueCountFrequency (%)
False 8098
91.2%
True 781
 
8.8%
2023-12-31T13:31:04.984922image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

state_top_Other
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.0 KiB
False
7569 
True
1310 
ValueCountFrequency (%)
False 7569
85.2%
True 1310
 
14.8%
2023-12-31T13:31:05.164953image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

state_top_TX
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.0 KiB
False
8574 
True
 
305
ValueCountFrequency (%)
False 8574
96.6%
True 305
 
3.4%
2023-12-31T13:31:05.319684image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

state_top_WA
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.0 KiB
False
8726 
True
 
153
ValueCountFrequency (%)
False 8726
98.3%
True 153
 
1.7%
2023-12-31T13:31:05.474015image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.0 KiB
False
7561 
True
1318 
ValueCountFrequency (%)
False 7561
85.2%
True 1318
 
14.8%
2023-12-31T13:31:05.634252image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

industry_2. Tech - Software Publisher
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.0 KiB
False
8556 
True
 
323
ValueCountFrequency (%)
False 8556
96.4%
True 323
 
3.6%
2023-12-31T13:31:05.790958image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

industry_3. Finance
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.0 KiB
False
8187 
True
 
692
ValueCountFrequency (%)
False 8187
92.2%
True 692
 
7.8%
2023-12-31T13:31:05.944859image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

industry_4. Consulting
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.0 KiB
False
8511 
True
 
368
ValueCountFrequency (%)
False 8511
95.9%
True 368
 
4.1%
2023-12-31T13:31:06.113134image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

industry_5. Retail
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.0 KiB
False
8325 
True
 
554
ValueCountFrequency (%)
False 8325
93.8%
True 554
 
6.2%
2023-12-31T13:31:06.271671image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

industry_6. Manufacturing
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.0 KiB
False
8256 
True
 
623
ValueCountFrequency (%)
False 8256
93.0%
True 623
 
7.0%
2023-12-31T13:31:06.423454image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

industry_7. Wholesale Trade
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.0 KiB
False
8562 
True
 
317
ValueCountFrequency (%)
False 8562
96.4%
True 317
 
3.6%
2023-12-31T13:31:06.580700image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

industry_8. Healthcare
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.0 KiB
False
8575 
True
 
304
ValueCountFrequency (%)
False 8575
96.6%
True 304
 
3.4%
2023-12-31T13:31:06.742219image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

industry_9. Other
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.0 KiB
False
5364 
True
3515 
ValueCountFrequency (%)
False 5364
60.4%
True 3515
39.6%
2023-12-31T13:31:06.896264image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

industry_nan
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.0 KiB
False
8014 
True
865 
ValueCountFrequency (%)
False 8014
90.3%
True 865
 
9.7%
2023-12-31T13:31:07.051866image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

revenue_Over 10B
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.0 KiB
False
8752 
True
 
127
ValueCountFrequency (%)
False 8752
98.6%
True 127
 
1.4%
2023-12-31T13:31:07.207158image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

revenue_Under 100M
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.0 KiB
False
8606 
True
 
273
ValueCountFrequency (%)
False 8606
96.9%
True 273
 
3.1%
2023-12-31T13:31:07.363844image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

revenue_Under 10B
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.0 KiB
False
8525 
True
 
354
ValueCountFrequency (%)
False 8525
96.0%
True 354
 
4.0%
2023-12-31T13:31:07.519742image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

revenue_Under 10M
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.0 KiB
False
8822 
True
 
57
ValueCountFrequency (%)
False 8822
99.4%
True 57
 
0.6%
2023-12-31T13:31:07.713039image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

revenue_Under 1B
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.0 KiB
False
8259 
True
 
620
ValueCountFrequency (%)
False 8259
93.0%
True 620
 
7.0%
2023-12-31T13:31:07.873340image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

revenue_Under 1M
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.0 KiB
False
8864 
True
 
15
ValueCountFrequency (%)
False 8864
99.8%
True 15
 
0.2%
2023-12-31T13:31:08.048225image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

revenue_nan
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.0 KiB
True
7433 
False
1446 
ValueCountFrequency (%)
True 7433
83.7%
False 1446
 
16.3%
2023-12-31T13:31:08.238463image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

founded_After 2000
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.0 KiB
False
6078 
True
2801 
ValueCountFrequency (%)
False 6078
68.5%
True 2801
31.5%
2023-12-31T13:31:08.419541image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

founded_Before 2000
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.0 KiB
False
8086 
True
 
793
ValueCountFrequency (%)
False 8086
91.1%
True 793
 
8.9%
2023-12-31T13:31:08.586762image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

founded_nan
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.0 KiB
True
5285 
False
3594 
ValueCountFrequency (%)
True 5285
59.5%
False 3594
40.5%
2023-12-31T13:31:08.751050image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Correlations

2023-12-31T13:31:09.322426image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
company_revenue_bucketcountry_topcountry_top_AUcountry_top_BRcountry_top_CAcountry_top_DEcountry_top_FRcountry_top_GBcountry_top_INcountry_top_Missingcountry_top_NLcountry_top_Othercountry_top_UScrossbeam_product10_customercrossbeam_product11_customercrossbeam_product12_customercrossbeam_product13_customercrossbeam_product14_customercrossbeam_product15_customercrossbeam_product16_customercrossbeam_product17_customercrossbeam_product18_customercrossbeam_product19_customercrossbeam_product1_customercrossbeam_product20_customercrossbeam_product21_customercrossbeam_product22_customercrossbeam_product23_customercrossbeam_product2_customercrossbeam_product3_customercrossbeam_product4_customercrossbeam_product5_customercrossbeam_product6_customercrossbeam_product7_customercrossbeam_product8_customercrossbeam_product9_customerdnb_founded_time_groupedfounded_After 2000founded_Before 2000founded_nanhas_crossbeam_datahas_hg_datahg_product_100hg_product_101hg_product_102hg_product_103hg_product_104hg_product_105hg_product_106hg_product_107hg_product_108hg_product_109hg_product_110hg_product_111hg_product_112hg_product_113hg_product_114hg_product_115hg_product_116hg_product_117hg_product_118hg_product_119hg_product_120hg_product_121hg_product_122hg_product_123hg_product_124hg_product_125hg_product_126hg_product_127hg_product_128hg_product_129hg_product_130hg_product_131hg_product_132hg_product_133hg_product_134hg_product_135hg_product_136hg_product_137hg_product_138hg_product_139hg_product_140hg_product_141hg_product_142hg_product_143hg_product_144hg_product_145hg_product_146hg_product_147hg_product_148hg_product_27hg_product_28hg_product_29hg_product_30hg_product_31hg_product_32hg_product_33hg_product_34hg_product_35hg_product_36hg_product_37hg_product_38hg_product_39hg_product_40hg_product_41hg_product_42hg_product_43hg_product_44hg_product_45hg_product_46hg_product_47hg_product_48hg_product_49hg_product_50hg_product_51hg_product_52hg_product_53hg_product_54hg_product_55hg_product_56hg_product_57hg_product_58hg_product_59hg_product_60hg_product_61hg_product_62hg_product_63hg_product_64hg_product_65hg_product_66hg_product_67hg_product_68hg_product_69hg_product_70hg_product_71hg_product_72hg_product_73hg_product_74hg_product_75hg_product_76hg_product_77hg_product_78hg_product_79hg_product_80hg_product_81hg_product_82hg_product_83hg_product_84hg_product_85hg_product_86hg_product_87hg_product_88hg_product_89hg_product_90hg_product_91hg_product_92hg_product_93hg_product_94hg_product_95hg_product_96hg_product_97hg_product_98hg_product_99industry_1. Tech - Computer systems design and related servicesindustry_2. Tech - Software Publisherindustry_3. Financeindustry_4. Consultingindustry_5. Retailindustry_6. Manufacturingindustry_7. Wholesale Tradeindustry_8. Healthcareindustry_9. Otherindustry_groupedindustry_nanis_arr_over_12kis_self_servicerevenue_Over 10Brevenue_Under 100Mrevenue_Under 10Brevenue_Under 10Mrevenue_Under 1Brevenue_Under 1Mrevenue_nanstatestate_topstate_top_CAstate_top_COstate_top_FLstate_top_GAstate_top_ILstate_top_MAstate_top_Missingstate_top_NYstate_top_Otherstate_top_TXstate_top_WA
company_revenue_bucket1.0000.0650.0000.0660.0000.0480.0770.0980.0310.0610.0000.0000.0770.0970.0670.0760.0890.1190.0690.0620.0880.0510.1300.0810.0650.1080.0970.0770.0460.0950.0670.1520.1310.1290.0520.0900.2620.1160.2340.1370.0700.1740.1350.1880.3000.2390.1450.2100.0960.1020.1030.0690.0880.0690.2180.1030.1870.0650.1260.0920.0640.1960.1050.0760.1030.0810.2680.1700.0840.0930.0640.1500.1160.1710.2790.1290.0840.0710.0770.1360.1410.1550.1370.1420.0690.2470.1990.1000.0760.0640.0700.1030.1620.1240.1190.1550.1490.2230.2050.0980.1910.1940.2400.2530.0990.2530.2210.1390.2480.2170.1300.1690.1400.1140.1510.1560.1150.1610.0690.1050.0740.1110.0690.1030.0980.0850.1870.2530.1070.2460.0780.1610.1790.1210.1230.0880.1210.2130.1480.1830.2450.2240.1730.2140.1560.1960.0640.2070.1600.0640.1590.1750.1090.0790.0990.0640.1280.1030.0760.1760.2350.0870.1430.1580.1850.0760.0060.0550.0000.2070.0000.0660.0490.1280.1010.0370.1100.9990.9990.9990.9990.9990.9991.0000.0000.0480.0540.0410.0000.0000.0000.0000.0710.0000.1090.0000.054
country_top0.0651.0000.9990.9990.9990.9990.9990.9990.9990.9990.9990.9990.9990.1580.1520.1630.1500.1500.1510.1520.1540.1730.1570.1510.1890.1560.1500.1560.1500.1550.1500.1500.1530.1510.1570.1500.1980.3950.0610.3960.2130.0530.0480.0490.0610.0550.0450.0530.0390.0430.0570.0410.0450.0570.0570.0570.0540.0360.0410.0430.0330.0520.0430.0340.0570.0610.0610.0890.0410.0460.0370.1140.0520.0630.0510.0360.0450.0510.0420.0400.0390.0910.1070.0420.0430.0700.0800.0750.0430.0460.0380.0570.0630.0510.0490.0920.0730.0770.0770.0350.0610.0510.0580.0530.0390.0480.0570.0400.0550.0710.0480.0650.0720.0420.0450.0490.0510.0460.0390.0790.0390.0700.0340.0340.0400.0330.0440.0590.0390.0520.0530.0860.0450.0980.0510.0370.0440.0910.0450.0530.0610.0630.0450.0510.0520.0500.0330.0860.0460.0580.0450.0820.0940.0470.0530.0370.0460.0570.0340.0480.0580.0370.0360.0460.1260.0990.0650.0420.0340.0070.0730.0560.1010.0730.2660.1980.1890.0210.0240.0070.0000.0380.0360.0511.0000.3100.3930.1100.1220.0950.1300.1480.9840.2730.3660.1630.112
country_top_AU0.0000.9991.0000.0150.0360.0350.0290.0560.0160.0140.0240.0860.2220.0260.0000.0230.0000.0000.0100.0100.0070.0320.0000.0000.0000.0000.0000.0130.0000.0000.0000.0000.0000.0000.0000.0000.0000.0860.0290.1000.0000.0010.0000.0000.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.0110.0000.0000.0000.0000.0000.0000.0000.0140.0150.0320.0000.0080.0000.0200.0000.0000.0090.0000.0000.0000.0000.0060.0000.0000.0000.0000.0060.0000.0000.0140.0070.0110.0000.0000.0000.0000.0000.0000.0000.0270.0130.0030.0110.0000.0160.0160.0000.0130.0110.0000.0150.0000.0000.0270.0210.0000.0080.0000.0080.0000.0000.0280.0000.0200.0000.0000.0000.0000.0000.0090.0000.0000.0000.0000.0240.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0240.0000.0000.0090.0350.0220.0000.0170.0000.0000.0000.0000.0000.0000.0000.0000.0000.0240.0180.0280.0000.0070.0000.0000.0000.0180.0440.0000.0210.0170.0000.0000.0000.0000.0000.0000.0001.0000.2170.0860.0210.0240.0170.0260.0300.2190.0590.0800.0340.021
country_top_BR0.0660.9990.0151.0000.0150.0140.0100.0280.0000.0000.0040.0450.1190.0570.0570.0550.0540.0550.0550.0540.0550.0560.0560.0540.0640.0550.0550.0550.0550.0550.0540.0550.0550.0540.0570.0540.0000.0500.0220.0620.0590.0000.0000.0000.0000.0170.0000.0000.0000.0220.0030.0000.0000.0000.0280.0030.0290.0000.0130.0000.0000.0130.0000.0000.0030.0050.0010.0170.0000.0000.0000.0120.0000.0000.0090.0000.0000.0000.0000.0000.0000.0000.0090.0000.0000.0070.0000.0000.0050.0000.0000.0030.0080.0240.0000.0000.0000.0000.0000.0000.0000.0240.0000.0000.0000.0000.0010.0000.0000.0050.0320.0000.0000.0080.0000.0150.0010.0000.0000.0000.0000.0000.0000.0000.0150.0000.0120.0000.0000.0000.0040.0000.0220.0240.0070.0000.0000.0000.0000.0000.0000.0060.0030.0000.0090.0100.0000.0000.0160.0000.0220.0000.0020.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0290.0060.0150.0090.0180.0000.0140.0000.0240.0540.0260.0380.0500.0000.0120.0000.0000.0000.0000.0161.0000.1140.0450.0000.0040.0000.0070.0110.1170.0290.0420.0140.000
country_top_CA0.0000.9990.0360.0151.0000.0360.0300.0570.0160.0140.0240.0880.2250.0200.0170.0200.0160.0210.0160.0250.0160.0180.0160.0180.0170.0210.0200.0190.0160.0230.0160.0230.0210.0200.0180.0180.0370.0740.0000.0680.0190.0160.0130.0130.0060.0060.0080.0060.0110.0110.0110.0060.0100.0070.0140.0110.0080.0090.0060.0240.0060.0130.0070.0080.0110.0060.0060.0070.0120.0110.0230.0060.0100.0120.0080.0090.0090.0060.0070.0100.0140.0060.0140.0110.0210.0110.0270.0120.0100.0120.0100.0110.0150.0080.0110.0130.0080.0200.0060.0070.0070.0100.0170.0130.0130.0060.0100.0100.0280.0170.0090.0070.0140.0200.0070.0060.0250.0190.0260.0130.0100.0310.0080.0070.0060.0060.0080.0080.0140.0060.0080.0070.0160.0070.0220.0190.0060.0240.0090.0120.0070.0090.0110.0060.0200.0070.0060.0200.0090.0070.0060.0100.0190.0070.0060.0060.0130.0110.0070.0090.0090.0060.0070.0160.0070.0160.0080.0090.0000.0000.0000.0000.0090.0110.0270.0090.0000.0000.0000.0000.0000.0000.0000.0001.0000.2200.0880.0210.0240.0170.0260.0310.2220.0600.0820.0340.022
country_top_DE0.0480.9990.0350.0140.0361.0000.0290.0550.0150.0130.0230.0850.2190.0370.0340.0410.0340.0330.0330.0330.0360.0460.0440.0350.0500.0440.0340.0330.0350.0380.0340.0340.0440.0330.0390.0330.0570.1500.0000.1460.0320.0200.0230.0260.0240.0240.0250.0230.0240.0240.0250.0230.0390.0230.0240.0250.0250.0230.0240.0240.0230.0250.0250.0230.0250.0250.0260.0240.0250.0230.0230.0240.0240.0760.0310.0240.0360.0600.0450.0230.0240.0270.0260.0240.0230.0240.0240.0240.0230.0230.0230.0250.0290.0240.0240.0240.0240.0240.0250.0230.0250.0270.0230.0240.0240.0230.0290.0250.0250.0240.0280.0270.0250.0250.0270.0270.0230.0230.0240.0270.0230.0230.0230.0230.0240.0230.0300.0230.0230.0250.0230.0250.0230.0320.0230.0250.0240.0290.0240.0260.0310.0300.0280.0250.0230.0240.0230.0230.0260.0230.0230.0230.0260.0260.0250.0230.0240.0250.0230.0230.0250.0240.0240.0250.0770.0170.0380.0000.0000.0060.0410.0150.0240.1010.0000.0440.0600.0010.0000.0000.0000.0260.0000.0181.0000.2130.0850.0200.0230.0160.0250.0300.2150.0580.0790.0330.021
country_top_FR0.0770.9990.0290.0100.0300.0291.0000.0470.0110.0090.0190.0730.1890.0000.0160.0160.0040.0000.0000.0000.0120.0250.0150.0100.0550.0240.0050.0000.0000.0150.0050.0000.0290.0330.0230.0000.0450.0980.0000.0920.0020.0180.0220.0220.0210.0220.0220.0230.0210.0210.0220.0200.0210.0200.0230.0220.0240.0210.0210.0210.0200.0220.0210.0210.0220.0240.0200.0200.0220.0220.0210.0220.0210.0210.0210.0210.0250.0200.0200.0220.0210.0340.0340.0240.0210.0240.0210.0280.0200.0220.0210.0220.0230.0210.0230.0310.0210.0200.0200.0210.0280.0220.0210.0210.0210.0260.0240.0220.0210.0250.0210.0210.0230.0200.0200.0210.0210.0220.0210.0210.0210.0230.0210.0200.0210.0200.0210.0250.0210.0200.0210.0210.0210.0260.0230.0200.0250.0310.0230.0220.0230.0280.0200.0220.0240.0230.0200.0210.0210.0200.0200.0200.0250.0210.0230.0200.0210.0220.0200.0200.0250.0210.0210.0210.0710.0570.0160.0000.0000.0000.0160.0150.0220.0960.0240.0400.0480.0000.0000.0040.0000.0140.0150.0161.0000.1840.0730.0160.0190.0120.0210.0250.1860.0500.0680.0280.016
country_top_GB0.0980.9990.0560.0280.0570.0550.0471.0000.0290.0260.0390.1300.3330.0130.0000.0290.0000.0060.0110.0000.0140.0390.0180.0000.0370.0060.0000.0000.0000.0120.0090.0000.0000.0000.0060.0000.0230.1150.0240.1250.0000.0000.0140.0150.0360.0330.0130.0130.0000.0000.0000.0330.0000.0000.0300.0000.0250.0000.0000.0000.0000.0280.0130.0000.0000.0210.0360.0460.0000.0000.0000.0340.0000.0050.0330.0000.0000.0000.0000.0000.0090.0410.0470.0140.0000.0420.0360.0150.0090.0000.0000.0000.0260.0230.0150.0330.0260.0430.0430.0000.0260.0190.0290.0230.0000.0080.0130.0000.0250.0390.0000.0280.0140.0000.0000.0220.0000.0000.0000.0090.0000.0230.0000.0000.0000.0000.0260.0400.0000.0280.0190.0160.0110.0460.0100.0000.0000.0430.0120.0220.0360.0390.0000.0280.0300.0220.0000.0410.0000.0000.0190.0390.0330.0090.0000.0000.0000.0000.0000.0240.0380.0000.0000.0000.0210.0320.0000.0200.0160.0000.0390.0000.0510.0750.0000.0000.0000.0250.0090.0100.0000.0000.0210.0001.0000.3260.1300.0350.0390.0300.0420.0480.3280.0900.1220.0530.036
country_top_IN0.0310.9990.0160.0000.0160.0150.0110.0291.0000.0000.0060.0470.1240.0200.0210.0210.0240.0230.0320.0200.0200.0230.0220.0200.0250.0230.0200.0320.0230.0200.0230.0200.0260.0200.0270.0290.0250.0390.0000.0310.0230.0050.0290.0050.0050.0050.0260.0070.0220.0210.0100.0050.0260.0670.0090.0100.0090.0060.0120.0230.0050.0080.0100.0060.0100.0090.0050.0050.0060.0190.0060.0200.0540.0160.0270.0070.0230.0050.0050.0070.0130.0170.0330.0110.0060.0070.0060.0100.0110.0050.0070.0100.0110.0060.0100.0250.0180.0060.0130.0080.0070.0140.0120.0080.0160.0050.0050.0060.0080.0200.0300.0150.0070.0290.0070.0050.0380.0050.0070.0130.0280.0060.0060.0050.0140.0050.0060.0150.0060.0050.0080.0050.0060.0260.0120.0050.0100.0250.0240.0090.0050.0110.0060.0090.0070.0070.0050.0210.0120.0670.0180.0160.0240.0100.0140.0050.0350.0100.0050.0270.0050.0160.0110.0090.0220.0160.0200.0000.0000.0050.0350.0000.0190.0560.0000.0370.0220.0000.0070.0000.0000.0000.0000.0001.0000.1190.0470.0000.0060.0000.0080.0120.1220.0310.0430.0150.002
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hg_product_910.0640.0370.0000.0000.0060.0230.0200.0000.0050.0430.0000.0190.0150.1260.0960.1250.1100.1520.0950.1070.1170.0950.1720.1220.1680.0980.1090.1070.1190.0950.0980.1440.1680.1320.1610.1060.3070.0230.2790.1870.1300.9700.7070.7070.7070.7070.7070.7090.7070.7071.0000.7070.7070.7070.7101.0000.7100.7070.7070.7070.7070.7090.7070.7071.0000.7070.7070.7070.7070.7070.7070.7070.7070.7070.7080.7070.7070.7070.7070.7070.7070.7070.7070.7120.7070.7080.7070.7070.7070.7070.7071.0000.7080.7070.7070.7070.7070.7070.7070.7070.7070.7090.7080.7080.7070.7080.7080.7070.7080.7080.7070.7070.7070.7070.7070.7090.7070.7070.7070.7070.7070.7080.7070.7070.7070.7070.7090.7080.7070.7070.7070.7070.7080.7070.7070.7070.7070.7070.7070.7070.7070.7080.7100.7090.7070.7090.7070.7070.7070.8660.7070.7070.7070.7070.7071.0000.7071.0000.7070.7110.7080.7070.7070.7070.0610.0510.0530.0270.0000.1130.0580.0000.0160.1050.1460.3000.3520.2030.3490.3880.1680.5160.0850.8370.0900.0240.0000.0000.0000.0000.0040.0190.0200.0170.0410.0000.000
hg_product_920.1280.0460.0000.0000.0130.0240.0210.0000.0350.0430.0000.0170.0220.1260.0960.1370.1120.1360.0950.1070.1260.0970.1750.1340.1680.0980.1120.1070.1190.0950.1010.1490.1710.1320.1610.1110.3090.0280.2790.1870.1300.9700.7280.7320.7160.7300.7280.7370.7250.7111.0000.7070.7130.7380.7331.0000.7390.7170.7510.7270.7230.7310.7070.7111.0000.7160.7150.7100.7300.7170.7070.7090.7120.7140.7130.7250.7120.7070.7070.7080.7120.7100.7090.7220.7090.7140.7100.7070.7090.7070.7071.0000.7120.7090.7130.7080.7090.7110.7110.7100.7120.7170.7140.7160.7130.7140.7150.7140.7140.7110.7090.7070.7070.7100.7090.7170.7090.7190.7070.7090.7080.7090.7070.7070.7070.7070.7240.7190.7070.7200.7090.7090.7110.7080.7130.7160.7100.7100.7130.7200.7180.7210.7190.7190.7200.7110.7070.7120.7090.7150.7080.7100.7100.7100.7090.7071.0001.0000.7150.7420.7220.7140.7320.7220.0620.0530.0570.0270.0200.1130.0220.0250.0200.1040.1460.3000.3530.2500.3490.3880.1680.5180.0860.8370.0510.0380.0000.0000.0000.0000.0100.0190.0250.0250.0430.0000.024
hg_product_930.1030.0570.0000.0030.0110.0250.0220.0000.0100.0430.0000.0000.0140.1790.1370.1770.1560.1860.1350.1520.1660.1350.2440.1740.2370.1390.1550.1510.1690.1350.1400.2050.2370.1870.2280.1500.3070.0240.2780.1860.1300.9701.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0610.0510.0530.0280.0000.1130.0200.0040.0160.1420.1460.2990.3520.2020.3490.3870.1660.5150.0820.8360.0900.0460.0000.0000.0000.0000.0100.0200.0180.0190.0410.0000.000
hg_product_940.0760.0340.0000.0000.0070.0230.0200.0000.0050.0430.0000.0000.0160.1280.0960.1250.1100.1310.0950.1070.1700.0950.1750.1860.1680.0980.1090.1080.1190.0950.0980.1440.1670.1320.1620.1690.3090.0240.2810.1870.1300.9700.7100.7190.7080.7080.7070.7110.7200.7071.0000.7070.7070.7900.7131.0000.7080.7070.7070.7210.7070.7110.7070.7071.0000.7080.7080.7070.7100.7100.7070.7070.7070.7070.7090.7200.7210.7070.7070.7230.7070.7080.7070.7070.7070.7070.7070.7070.7070.7070.7071.0000.7070.7090.7090.7070.7070.7080.7070.7110.7070.7080.7070.7070.7160.7070.7070.7070.7070.7070.7070.7070.7070.7080.7070.7080.7070.7100.7070.7080.7070.7090.7070.7070.7070.7070.7080.7090.7070.7080.7080.7070.7100.7080.7070.7070.7070.7070.7090.7080.7070.7070.7080.7080.7090.7080.7070.7070.7170.7070.7070.7070.7070.7070.7080.7070.7151.0001.0000.7090.7100.7160.7130.7120.0610.0620.0530.0270.0000.1150.0190.0000.0180.1030.1460.3000.3530.2030.3500.3930.1680.5160.0850.8370.0550.0370.0000.0000.0000.0000.0040.0190.0200.0170.0430.0350.000
hg_product_950.1760.0480.0000.0000.0090.0230.0200.0240.0270.0430.0000.0190.0320.1260.0970.1380.1190.1660.0950.1070.1260.1010.1770.1300.1700.1000.1120.1060.1200.0950.1000.1540.1690.1530.1610.1090.3150.0370.2830.1860.1300.9700.7540.7360.7410.7880.7440.7890.7240.7081.0000.7110.7190.7140.7721.0000.7850.7120.7310.7170.7070.8180.7140.7151.0000.7100.7440.7190.7290.7280.7070.7120.7080.7180.7340.7200.7110.7070.7090.7400.7200.7180.7150.7400.7070.7360.7170.7120.7070.7070.7071.0000.7360.7220.7440.7180.7130.7280.7180.7140.7240.7300.7320.7330.7230.7340.7330.7220.7330.7250.7080.7110.7090.7120.7180.7290.7190.7290.7070.7130.7100.7110.7080.7070.7100.7070.7480.7470.7080.7510.7080.7140.7400.7130.7150.7100.7100.7160.7320.7390.7380.7440.7360.7360.7240.7330.7070.7220.7320.7140.7120.7190.7090.7080.7080.7110.7421.0000.7091.0000.7510.7150.7290.7400.0610.0570.0630.0270.0000.1200.0190.0040.0230.1070.1460.3000.3530.2870.3530.3920.1690.5190.0880.8370.0570.0380.0000.0000.0000.0000.0050.0210.0350.0170.0490.0000.015
hg_product_960.2350.0580.0000.0000.0090.0250.0250.0380.0050.0430.0000.0250.0630.1290.0970.1390.1130.1500.0950.1070.1210.0980.2010.1330.1720.1000.1160.1060.1190.0960.0990.1580.1730.1450.1610.1220.3210.0460.2870.1860.1300.9700.7400.7870.7770.7820.7320.7820.7150.7241.0000.7080.7110.7100.7871.0000.7830.7100.7260.7180.7070.7760.7130.7101.0000.7160.7800.7330.7340.7290.7080.7190.7120.7140.7410.7170.7120.7070.7100.7180.7110.7390.7330.7360.7090.7590.7350.7120.7070.7070.7071.0000.7480.7200.7450.7330.7250.7500.7360.7140.7470.7400.7370.7420.7120.7480.7500.7180.7390.7410.7150.7190.7160.7250.7230.7290.7130.7240.7070.7190.7100.7230.7080.7070.7080.7070.7660.7710.7130.7700.7100.7230.7440.7250.7270.7160.7130.7300.7310.7530.7540.7670.7400.7500.7390.7450.7070.7390.7270.7100.7140.7310.7140.7120.7110.7080.7221.0000.7100.7511.0000.7160.7310.7250.0620.0530.0630.0270.0120.1300.0200.0040.0230.1120.1460.3020.3560.3070.3630.4190.1690.5220.0930.8380.0650.0540.0070.0000.0000.0160.0270.0260.0640.0220.0490.0000.000
hg_product_970.0870.0370.0000.0000.0060.0240.0210.0000.0160.0430.0000.0000.0130.1270.0960.1270.1250.1380.0950.1070.1280.0960.1740.1360.1680.1000.1090.1060.1200.0950.1020.1560.1680.1350.1610.1120.3090.0250.2800.1860.1300.9700.7310.7130.7140.7180.7150.7280.7120.7271.0000.7250.7140.7430.7231.0000.7240.7070.7090.7430.7070.7280.7110.7071.0000.7080.7150.7090.7180.7100.7070.7080.7130.7080.7140.7070.7200.7070.7070.7260.7070.7100.7090.7240.7090.7130.7090.7120.7070.7090.7071.0000.7160.7260.7240.7100.7080.7110.7090.7180.7130.7170.7120.7130.7380.7160.7140.7250.7140.7130.7090.7100.7070.7110.7120.7170.7210.7170.7070.7090.7150.7120.7070.7070.7100.7070.7190.7170.7070.7250.7100.7090.7140.7090.7100.7080.7130.7090.7220.7200.7150.7140.7190.7150.7140.7170.7070.7090.7220.7160.7190.7090.7110.7120.7120.7070.7141.0000.7160.7150.7161.0000.7280.7250.0620.0520.0530.0320.0040.1150.0210.0040.0140.1020.1460.3000.3520.2110.3500.3900.1690.5160.0850.8370.0630.0330.0000.0000.0000.0070.0090.0260.0180.0220.0410.0000.000
hg_product_980.1430.0360.0000.0000.0070.0240.0210.0000.0110.0430.0000.0000.0190.1280.0970.1410.1190.1440.0970.1080.1230.1040.1820.1380.1680.1080.1290.1070.1190.0950.1000.1680.1730.1480.1610.1090.3080.0220.2800.1870.1300.9700.7260.7390.7190.7250.7180.7230.7210.7101.0000.7070.7080.7310.7291.0000.7300.7140.7330.7310.7070.7380.7090.7071.0000.7140.7180.7100.7240.7170.7100.7090.7080.7160.7170.7140.7100.7070.7130.7270.7110.7100.7100.7230.7080.7180.7110.7090.7070.7070.7071.0000.7150.7160.7200.7110.7090.7140.7110.7110.7150.7200.7170.7200.7270.7160.7170.7220.7210.7140.7080.7080.7080.7090.7130.7150.7100.7310.7070.7100.7080.7110.7070.7070.7080.7070.7310.7220.7080.7240.7090.7090.7170.7100.7160.7140.7120.7100.7190.7200.7170.7230.7200.7290.7120.7190.7190.7130.7160.7130.7100.7100.7100.7090.7070.7070.7321.0000.7130.7290.7310.7281.0000.7340.0610.0510.0570.0300.0000.1170.0230.0080.0210.1030.1460.3000.3530.2540.3500.3890.1700.5200.0860.8370.0640.0460.0000.0000.0000.0000.0110.0390.0230.0170.0430.0000.035
hg_product_990.1580.0460.0000.0000.0160.0250.0210.0000.0090.0430.0000.0230.0420.1260.0970.1380.1170.1620.0950.1080.1340.0960.1760.1240.1680.1000.1140.1070.1190.0950.1000.1530.1680.1400.1610.1090.3100.0250.2810.1870.1300.9700.7360.7340.7210.7390.7200.7380.7130.7241.0000.7070.7080.7280.7451.0000.7330.7070.7150.7370.7070.7310.7170.7071.0000.7120.7210.7110.7210.7110.7070.7090.7150.7150.7250.7100.7100.7070.7070.7450.7160.7130.7130.7350.7070.7240.7110.7090.7070.7070.7071.0000.7190.7310.7430.7120.7110.7160.7140.7120.7170.7230.7140.7200.7670.7220.7210.7430.7230.7160.7080.7120.7090.7130.7130.7270.7200.7650.7070.7120.7110.7140.7070.7070.7130.7070.7300.7260.7070.7400.7110.7090.7210.7140.7140.7090.7090.7100.7170.7300.7250.7250.7470.7250.7170.7270.7070.7150.7190.7120.7110.7120.7100.7090.7080.7070.7221.0000.7120.7400.7250.7250.7341.0000.0630.0540.0540.0270.0000.1370.0190.0100.0170.1140.1460.3000.3530.2650.3510.3890.1680.5200.0860.8370.0630.0490.0000.0000.0000.0210.0040.0270.0440.0200.0460.0000.031
industry_1. Tech - Computer systems design and related services0.1850.1260.0240.0290.0070.0770.0710.0210.0220.0400.0000.0090.0360.1340.0570.0560.0540.0540.0820.0520.0550.0530.0520.0550.0550.0540.0520.0970.0520.0760.0570.0610.0520.0580.0530.0540.1720.1960.0590.1500.0530.0620.0610.0630.0650.0610.0610.0620.0620.0620.0610.0610.0610.0610.0650.0610.0630.0610.0610.0620.0610.0610.0620.0610.0610.0750.0660.0680.0620.0610.0610.0610.0610.0620.0690.0620.0620.0610.0610.0610.0610.0610.0610.0610.0630.0620.0630.0690.0640.0620.0680.0610.0660.0630.0610.0630.0620.0610.0650.0610.0620.0610.0640.0610.0610.0620.0630.0610.0610.0610.0610.0610.0610.0670.0640.0620.0610.0620.0610.0760.0610.0650.0610.0630.0610.0610.0620.0610.0610.0630.0610.0630.0610.0800.0610.0610.0610.0660.0620.0640.0630.0620.0640.0630.0630.0610.0660.0630.0610.0610.0620.0620.0780.0660.0730.0610.0620.0610.0610.0610.0620.0620.0610.0631.0000.0800.1200.0850.1070.1140.0790.0770.3381.0000.1360.0050.0110.0400.0150.0640.0000.0330.0000.0600.0780.0640.0230.0050.0250.0140.0000.0000.0340.0260.0420.0000.005
industry_2. Tech - Software Publisher0.0760.0990.0180.0060.0160.0170.0570.0320.0160.0130.0190.0440.0600.1410.0630.0570.0490.0690.0530.0750.0580.0560.0820.0680.0960.0710.0480.1010.0590.0710.0480.0500.0640.0490.0880.0520.0170.0780.0050.0810.0480.0450.0510.0510.0520.0540.0510.0550.0520.0520.0510.0510.0520.0510.0570.0510.0590.0510.0510.0520.0510.0520.0510.0510.0510.0620.0510.0800.0510.0510.0510.0690.0520.0510.0540.0540.0520.0510.0510.0510.0510.0790.0800.0550.0610.0530.0730.0990.0760.0720.0510.0510.0790.0600.0510.0760.0730.0790.0880.0620.0610.0510.0540.0540.0520.0530.0510.0510.0520.0540.0510.0550.0570.0540.0590.0510.0510.0510.0560.0700.0580.0740.0510.0510.0510.0740.0570.0550.0520.0530.0570.0700.0530.1240.0510.0530.0580.0510.0510.0580.0610.0560.0510.0550.0510.0530.0510.0760.0510.0510.0510.0760.1050.0850.0670.0510.0530.0510.0620.0570.0530.0520.0510.0540.0801.0000.0540.0370.0480.0510.0340.0330.1561.0000.0620.0800.0870.0000.0280.0090.0000.0750.0000.0690.0570.0920.0780.0000.0070.0210.0000.0240.0580.0130.0000.0000.000
industry_3. Finance0.0060.0650.0280.0150.0080.0380.0160.0000.0200.0160.0080.0000.0260.0640.0420.0510.0420.0410.0420.0450.0450.0420.0520.0500.0690.0410.0460.0730.0430.0440.0500.1030.0460.0620.0510.0460.0290.0200.0100.0070.0430.0560.0530.0560.0630.0620.0530.0620.0600.0590.0530.0530.0580.0530.0720.0530.0610.0570.0530.0530.0530.0570.0580.0530.0530.0540.0680.0560.0610.0660.0540.0530.0530.0560.0540.0550.0530.0530.0530.0540.0530.0600.0590.0530.0530.0560.0680.0630.0530.0540.0530.0530.0550.0530.0590.0560.0540.0590.0580.0540.0590.0590.0760.0600.0540.0570.0620.0530.0590.0550.0550.0650.0530.0530.0550.0530.0530.0530.0530.0540.0540.0530.0530.0530.0560.0530.0820.0850.0530.0720.0570.0600.0530.0530.0530.0530.0550.0650.0590.0600.0670.0760.0630.0750.0530.0640.0530.0540.0590.0530.0530.0540.0740.0590.0570.0530.0570.0530.0530.0630.0630.0530.0570.0540.1200.0541.0000.0580.0730.0780.0540.0530.2351.0000.0940.0660.0740.0000.0000.0320.0000.0490.0000.0590.0450.0440.0000.0120.0000.0270.0090.0050.0280.0100.0180.0000.000
industry_4. Consulting0.0550.0420.0000.0090.0090.0000.0000.0200.0000.0150.0000.0040.0470.0000.0000.0000.0160.0000.0160.0000.0000.0000.0120.0000.0000.0000.0000.0240.0000.0000.0000.0000.0000.0000.0180.0000.0410.0180.0220.0000.0000.0270.0270.0270.0270.0270.0270.0280.0280.0280.0280.0270.0280.0270.0270.0280.0270.0270.0280.0280.0270.0270.0270.0270.0280.0330.0270.0280.0300.0270.0360.0280.0280.0280.0270.0280.0290.0270.0270.0410.0270.0270.0270.0330.0270.0290.0290.0270.0270.0270.0270.0280.0280.0270.0300.0280.0310.0300.0290.0270.0280.0280.0290.0270.0280.0310.0280.0270.0270.0300.0270.0320.0300.0270.0290.0270.0270.0290.0290.0300.0410.0300.0270.0270.0270.0270.0300.0270.0270.0300.0280.0270.0270.0310.0350.0330.0290.0290.0340.0310.0330.0270.0270.0300.0330.0290.0270.0270.0270.0270.0270.0280.0290.0280.0280.0270.0270.0280.0270.0270.0270.0320.0300.0270.0850.0370.0581.0000.0510.0550.0370.0360.1671.0000.0670.0130.0030.0140.0000.0270.0000.0200.0000.0370.0000.0440.0100.0000.0000.0000.0000.0000.0480.0000.0180.0250.000
industry_5. Retail0.0000.0340.0070.0180.0000.0000.0000.0160.0000.0220.0040.0090.0020.0480.0360.0320.0340.0360.0490.0320.0340.0360.0320.0330.0340.0320.0350.0500.0320.0320.0320.0320.0340.0320.0360.0340.0060.0290.0000.0270.0340.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0110.0000.0000.0000.0000.0000.0000.0070.0000.0000.0000.0090.0100.0260.0000.0000.0100.0000.0000.0000.0090.0060.0000.0000.0000.0150.0030.0000.0000.0000.0000.0000.0090.0000.0000.0000.0000.0000.0210.0000.0230.0000.0210.0260.0000.0130.0000.0130.0110.0020.0000.0190.0000.0000.0090.0000.0000.0130.0120.0370.0070.0000.0000.0000.0000.0280.0000.0190.0000.0000.0070.0000.0090.0000.0000.0220.0110.0000.0160.0260.0000.0000.0310.0000.0210.0000.0000.0130.0000.0000.0110.0000.0000.0000.0000.0000.0080.0000.0110.0060.0080.0000.0200.0000.0000.0000.0120.0040.0000.0000.1070.0480.0730.0511.0000.0690.0470.0460.2081.0000.0830.0490.0350.0140.0000.0000.0000.0000.0000.0110.0270.0310.0000.0000.0000.0160.0000.0000.0030.0220.0000.0000.023
industry_6. Manufacturing0.2070.0070.0000.0000.0000.0060.0000.0000.0050.0070.0000.0000.0000.0400.0330.0450.0270.0360.0330.0330.0390.0280.0530.0520.0400.0270.0280.0290.0280.0370.0360.0360.0420.0500.0420.0290.1590.0000.1320.0680.0270.1130.1130.1190.1290.1250.1130.1250.1160.1150.1130.1130.1180.1240.1230.1130.1250.1130.1130.1200.1130.1240.1470.1130.1130.1150.1280.1130.1140.1130.1130.1160.1210.1290.1800.1140.1170.1180.1240.1200.1200.1140.1150.1200.1140.1170.1130.1140.1150.1130.1130.1130.1130.1280.1180.1130.1130.1130.1130.1150.1130.1180.1200.1250.1240.1200.1200.1170.1190.1170.1140.1130.1130.1150.1170.1130.1130.1140.1130.1130.1130.1140.1130.1130.1160.1130.1160.1190.1140.1190.1140.1140.1150.1130.1150.1150.1150.1270.1200.1180.1210.1230.1260.1170.1140.1150.1130.1140.1130.1150.1130.1140.1140.1130.1130.1130.1130.1130.1150.1200.1300.1150.1170.1370.1140.0510.0780.0550.0691.0000.0510.0490.2221.0000.0890.0510.0680.1020.0000.1230.0000.0470.0000.1270.0900.0410.0000.0250.0000.0000.0000.0060.0090.0100.0350.0000.000
industry_7. Wholesale Trade0.0000.0730.0000.0140.0000.0410.0160.0390.0350.0130.0000.0000.0000.0240.0050.0080.0050.0050.0360.0190.0130.0070.0200.0150.0130.0190.0060.0220.0080.0270.0220.0200.0090.0060.0090.0090.0560.0110.0570.0470.0110.0190.0190.0190.0210.0180.0190.0190.0210.0190.0200.0180.0200.0190.0190.0200.0190.0190.0230.0200.0180.0220.0320.0190.0200.0340.0190.0220.0180.0260.0190.0200.0200.0210.0210.0210.0200.0180.0190.0250.0180.0320.0220.0240.0250.0240.0220.0180.0180.0190.0200.0200.0260.0260.0290.0300.0300.0250.0300.0190.0200.0220.0180.0190.0220.0220.0240.0250.0180.0240.0220.0190.0190.0210.0250.0180.0220.0220.0200.0290.0200.0270.0300.0190.0220.0180.0230.0180.0200.0190.0240.0220.0200.0350.0250.0260.0240.0320.0230.0240.0210.0260.0240.0280.0180.0200.0180.0250.0230.0410.0180.0270.0200.0180.0200.0580.0220.0200.0190.0190.0200.0210.0230.0190.0790.0340.0540.0370.0470.0511.0000.0330.1551.0000.0610.0460.0340.0170.0000.0000.0000.0140.0000.0160.0280.0000.0040.0000.0090.0000.0000.0000.0030.0000.0190.0000.000
industry_8. Healthcare0.0660.0560.0000.0000.0000.0150.0150.0000.0000.0120.0170.0360.0520.0130.0140.0100.0030.0120.0320.0130.0130.0070.0150.0150.0100.0090.0100.0050.0070.0130.0120.0060.0060.0100.0380.0080.0130.0360.0000.0360.0110.0100.0150.0000.0200.0050.0000.0110.0000.0000.0040.0000.0070.0000.0000.0040.0000.0000.0000.0000.0000.0100.0000.0230.0040.0100.0270.0000.0000.0000.0000.0000.0000.0040.0260.0000.0000.0000.0000.0120.0070.0000.0000.0000.0000.0030.0090.0000.0070.0000.0000.0040.0110.0120.0100.0150.0270.0000.0170.0000.0290.0000.0000.0000.0000.0000.0000.0130.0000.0170.0000.0000.0000.0120.0110.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0000.0000.0210.0000.0080.0000.0160.0050.0000.0160.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0190.0170.0000.0000.0000.0000.0130.0000.0000.0250.0040.0000.0040.0040.0040.0080.0100.0770.0330.0530.0360.0460.0490.0331.0000.1511.0000.0600.0200.0200.0000.0040.0100.0040.0000.0100.0000.0580.0660.0000.0000.0210.0000.0000.0000.0530.0510.0130.0000.000
industry_9. Other0.0490.1010.0180.0240.0090.0240.0220.0510.0190.0630.0080.0280.0370.0470.0310.0240.0220.0240.0230.0300.0340.0310.0430.0280.0430.0270.0220.0370.0220.0240.0260.0250.0260.0320.0280.0230.0350.0350.0070.0250.0210.0150.0130.0170.0220.0260.0150.0230.0160.0140.0160.0180.0200.0180.0200.0160.0190.0140.0130.0140.0180.0180.0420.0140.0160.0170.0180.0370.0180.0250.0150.0380.0140.0140.0250.0200.0130.0160.0180.0130.0260.0450.0480.0200.0140.0250.0470.0270.0310.0260.0180.0160.0270.0200.0150.0280.0200.0320.0470.0130.0270.0180.0210.0300.0210.0200.0190.0150.0190.0270.0170.0140.0260.0170.0130.0180.0170.0140.0170.0270.0180.0280.0140.0130.0150.0160.0220.0340.0140.0280.0130.0450.0130.0570.0180.0140.0130.0210.0210.0140.0240.0230.0310.0250.0180.0230.0160.0310.0130.0180.0130.0330.0300.0240.0260.0160.0200.0160.0180.0230.0230.0140.0210.0170.3380.1560.2350.1670.2080.2220.1550.1511.0001.0000.2650.0400.0380.0110.0000.0000.0130.0000.0100.0000.0000.0410.0340.0000.0000.0000.0000.0050.0400.0000.0100.0090.000
industry_grouped0.1280.0730.0440.0540.0110.1010.0960.0750.0560.0180.0320.0650.1100.1390.0540.0550.0420.0550.0750.0590.0540.0460.0750.0640.0830.0550.0400.1070.0440.0700.0490.0800.0550.0610.0770.0440.2330.1990.1500.1720.0620.1420.0990.1030.1130.1090.0990.1100.1030.1020.1420.0980.1040.1050.1130.1420.1100.1000.0990.1030.0980.1070.1230.1000.1420.1100.1140.1140.1020.1040.1010.1070.1040.1090.1420.1020.1020.1010.1050.1070.1040.1130.1130.1050.1030.1040.1140.1200.1090.1060.1010.1420.1120.1100.1060.1100.1110.1130.1180.1040.1060.1040.1110.1090.1050.1060.1060.1020.1040.1050.1000.1040.1020.1060.1060.0990.0990.1000.1010.1130.1040.1100.1000.0990.1020.1050.1120.1150.0990.1110.1030.1100.1010.1380.1020.1010.1050.1130.1070.1070.1110.1140.1110.1110.1010.1040.1000.1100.1010.1030.0990.1100.1290.1130.1090.1050.1040.1420.1030.1070.1120.1020.1030.1141.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.1300.1340.1110.0200.1390.0000.1030.0120.1640.0560.0560.0880.0220.0340.0380.0000.0220.1100.0650.0660.0180.023
industry_nan0.1010.2660.0000.0260.0270.0000.0240.0000.0000.2610.0000.0000.0440.2610.2590.2590.2580.2580.2580.2590.2580.2580.2600.2580.2750.2580.2580.2660.2580.2580.2580.2580.2600.2580.2620.2581.0000.2220.1020.2700.2550.1420.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1460.1360.0620.0940.0670.0830.0890.0610.0600.2651.0001.0000.1760.1960.0300.0540.0650.0100.0870.0000.1380.0830.0500.0110.0000.0000.0150.0230.0060.0470.0110.0000.0160.000
is_arr_over_12k0.0370.1980.0210.0380.0090.0440.0400.0000.0370.0680.0280.1220.1810.2470.2480.2280.2170.2220.2220.2370.2240.2320.2420.2220.3600.2310.2170.2510.2340.2310.2200.2260.2300.2200.2860.2170.0990.0420.0970.0980.2160.2930.3000.3010.3020.3000.3000.3010.3000.3000.2990.3000.3000.3000.3020.2990.3010.3000.3000.3000.3000.3010.3000.3000.2990.3020.3040.3100.3020.3000.3000.3070.3000.3000.3010.3000.3000.3000.3000.3000.3000.3130.3160.3000.3000.3020.3070.3040.3010.3010.3000.2990.3040.3020.3010.3060.3020.3040.3090.3000.3030.3010.3010.3010.3000.3010.3030.3000.3000.3030.3000.3030.3010.3010.3020.3000.3010.3000.3000.3010.3000.3010.3000.3000.3000.3000.3020.3020.3000.3000.3030.3100.3000.3070.3010.3000.3030.3020.3000.3010.3010.3000.3000.3000.3000.3010.3000.3060.3000.3000.3000.3050.3050.3010.3020.3000.3000.2990.3000.3000.3020.3000.3000.3000.0050.0800.0660.0130.0490.0510.0460.0200.0400.1300.1761.0000.6880.0900.1050.1440.0440.1890.0000.2980.0560.1840.0870.0100.0000.0000.0250.0380.1820.0640.0510.0290.000
is_self_service0.1100.1890.0170.0500.0000.0600.0480.0000.0220.0670.0200.1080.1630.2350.2260.2120.1970.2030.1990.2230.2110.2090.2400.2070.3900.2090.1990.2290.2190.2140.2030.2100.2200.2060.2810.1980.1440.0500.1370.1290.1970.3480.3530.3540.3560.3540.3530.3540.3520.3520.3520.3520.3520.3520.3560.3520.3540.3530.3530.3530.3520.3550.3530.3520.3520.3540.3570.3600.3550.3530.3520.3600.3520.3520.3550.3530.3530.3520.3530.3520.3530.3630.3660.3530.3520.3560.3590.3540.3530.3530.3520.3520.3560.3530.3530.3570.3560.3570.3600.3530.3570.3540.3540.3550.3530.3530.3580.3530.3530.3550.3530.3530.3530.3540.3540.3540.3530.3530.3530.3530.3520.3530.3530.3530.3520.3520.3550.3550.3520.3530.3540.3630.3530.3570.3540.3530.3530.3550.3530.3530.3540.3550.3530.3550.3540.3540.3520.3580.3530.3520.3520.3570.3550.3530.3540.3520.3530.3520.3530.3530.3560.3520.3530.3530.0110.0870.0740.0030.0350.0680.0340.0200.0380.1340.1960.6881.0000.1140.1080.1920.0620.2210.0290.3610.0930.1700.0750.0200.0000.0000.0300.0510.1640.0510.0610.0000.000
revenue_Over 10B0.9990.0210.0000.0000.0000.0010.0000.0250.0000.0000.0000.0000.0270.0480.0440.0860.1100.1530.0420.0400.1190.0400.1430.0890.0810.0650.1140.0510.0420.0430.0400.2100.1640.1800.0710.1210.1860.0320.1630.0610.0260.1790.2540.3000.3150.3220.2550.3080.2180.2250.2020.2030.2140.2030.3220.2020.2840.2040.2480.2110.2030.2950.2180.2060.2020.2050.3050.2440.2090.2130.2030.2290.2340.2860.3570.2540.2050.2030.2100.2550.2450.2480.2340.2530.2040.3170.2560.2140.2050.2030.2040.2020.2630.2330.2290.2530.2250.2910.2680.2190.2680.2960.3230.3350.2240.3630.3140.2520.3560.2990.2480.2310.2100.2280.2430.2440.2300.2630.2040.2140.2070.2190.2060.2030.2080.2200.2800.3160.2310.3510.2060.2240.2730.2280.2330.2150.2320.2500.2460.2740.3240.3040.2680.2980.2510.3020.2030.2570.2590.2030.2620.2440.2200.2110.2120.2030.2500.2020.2030.2870.3070.2110.2540.2650.0400.0000.0000.0140.0140.1020.0170.0000.0110.1110.0300.0900.1141.0000.0150.0190.0000.0290.0000.2720.0730.0440.0130.0000.0000.0000.0000.0120.0250.0000.0320.0000.022
revenue_Under 100M0.9990.0240.0000.0120.0000.0000.0000.0090.0070.0110.0000.0150.0080.1300.0440.0440.0420.0480.0420.0610.0480.0430.0680.0450.0740.0670.0500.0720.0550.0460.0440.0450.0750.0430.0640.0490.0000.0400.0280.0560.0410.3600.3500.3540.3740.3580.3520.3580.3500.3500.3490.3490.3510.3500.3540.3490.3520.3500.3490.3490.3490.3550.3510.3500.3490.3500.3700.3550.3500.3510.3500.3490.3510.3520.3610.3510.3500.3530.3510.3500.3530.3540.3530.3510.3500.3570.3510.3490.3500.3500.3490.3490.3520.3540.3500.3490.3610.3540.3500.3510.3600.3520.3560.3630.3500.3550.3540.3500.3540.3570.3500.3550.3550.3500.3500.3530.3500.3500.3500.3490.3490.3500.3500.3500.3500.3490.3530.3620.3500.3580.3510.3500.3500.3530.3520.3500.3500.3670.3520.3530.3640.3610.3530.3540.3520.3560.3490.3520.3520.3500.3490.3530.3510.3490.3500.3490.3490.3490.3500.3530.3630.3500.3500.3510.0150.0280.0000.0000.0000.0000.0000.0040.0000.0200.0540.1050.1080.0151.0000.0330.0000.0460.0000.4030.0000.0160.0210.0000.0000.0000.0000.0000.0110.0000.0000.0100.000
revenue_Under 10B0.9990.0070.0000.0000.0000.0000.0040.0100.0000.0150.0000.0000.0120.0660.0600.0970.0630.1120.0600.0760.0920.0600.2100.1100.1290.0620.0680.0600.0710.0610.0620.1370.1280.1010.1130.0630.2130.0000.2180.1350.0600.3820.3900.3910.4640.4080.3900.4010.3890.3880.3870.3910.3880.3890.3990.3870.4020.3880.3890.3920.3880.4000.3950.3890.3870.3890.4450.3990.3920.3910.3880.4000.3880.3880.4220.3890.3890.3880.3880.3890.3890.4010.4000.3920.3890.4030.4180.3880.3880.3880.3880.3870.3940.3900.3950.3930.3960.4080.4140.3890.4070.3970.4130.4200.3880.3960.4070.3880.3990.4090.3890.4000.3940.3930.3950.4010.3880.3880.3890.3920.3890.3890.3880.3880.3890.3880.3990.4200.3880.3990.3880.4110.3950.3920.3920.3880.3890.4210.3900.3920.3960.4070.3920.4020.4010.3960.3880.4140.3930.3880.3880.4010.3920.3880.3880.3880.3880.3870.3930.3920.4190.3900.3890.3890.0640.0090.0320.0270.0000.1230.0000.0100.0000.1390.0650.1440.1920.0190.0331.0000.0070.0540.0000.4610.0680.0510.0000.0000.0010.0000.0090.0000.0160.0190.0510.0000.004
revenue_Under 10M0.9990.0000.0000.0000.0000.0000.0000.0000.0000.0000.0090.0000.0000.0330.0090.0270.0000.0000.0000.0000.0000.0100.0080.0500.0200.0000.0220.0180.0340.0400.0000.0500.0100.0400.0250.0000.0000.0090.0000.0150.0000.1710.1710.1690.1700.1770.1700.1720.1690.1690.1660.1680.1680.1680.1720.1660.1720.1680.1690.1680.1680.1690.1700.1680.1660.1760.1730.1690.1680.1680.1680.1690.1700.1680.1680.1690.1780.1680.1680.1680.1690.1680.1680.1690.1690.1730.1680.1700.1710.1690.1700.1660.1690.1680.1700.1690.1690.1680.1680.1720.1680.1700.1730.1700.1690.1690.1720.1680.1680.1680.1710.1680.1690.1680.1680.1680.1680.1710.1680.1680.1680.1680.1680.1680.1680.1680.1680.1770.1680.1690.1680.1700.1690.1730.1680.1690.1690.1810.1720.1710.1720.1710.1690.1700.1680.1680.1680.1680.1720.1680.1680.1680.1680.1690.1850.1680.1680.1660.1680.1690.1690.1690.1700.1680.0000.0000.0000.0000.0000.0000.0000.0040.0130.0000.0100.0440.0620.0000.0000.0071.0000.0160.0000.1800.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.000
revenue_Under 1B0.9990.0380.0000.0000.0000.0260.0140.0000.0000.0240.0000.0000.0200.1040.0810.1040.0850.0870.0870.0890.0910.0810.1160.1090.1470.0840.0830.0880.1050.0810.1070.0870.1300.0930.1510.0810.2050.0080.1970.1270.0820.5230.5170.5190.5250.5230.5180.5190.5170.5170.5150.5160.5160.5160.5220.5150.5210.5160.5160.5180.5160.5200.5170.5170.5150.5160.5210.5240.5160.5170.5160.5170.5170.5200.5290.5170.5160.5160.5160.5180.5190.5160.5160.5190.5170.5310.5240.5220.5160.5160.5160.5150.5190.5170.5180.5200.5200.5280.5260.5160.5200.5210.5220.5210.5160.5230.5210.5180.5240.5210.5160.5290.5230.5170.5220.5210.5180.5210.5160.5210.5170.5220.5160.5160.5200.5160.5220.5240.5160.5230.5170.5190.5230.5190.5170.5160.5200.5160.5200.5240.5250.5220.5220.5240.5210.5180.5160.5310.5200.5160.5230.5240.5180.5160.5160.5160.5180.5150.5160.5190.5220.5160.5200.5200.0330.0750.0490.0200.0000.0470.0140.0000.0000.1030.0870.1890.2210.0290.0460.0540.0161.0000.0000.6210.0650.0470.0000.0080.0000.0000.0000.0290.0230.0070.0360.0000.000
revenue_Under 1M0.9990.0360.0000.0000.0000.0000.0150.0210.0000.0000.0000.0000.0190.0030.0210.0330.0000.0000.0060.0030.0000.0060.0060.0000.0100.0060.0270.0030.0000.0040.0000.0240.0150.0000.0000.0000.0000.0000.0000.0000.0000.0850.0870.0870.1020.0910.0860.0900.0850.0850.0820.0850.0850.0850.0890.0820.0890.0850.0860.0850.0850.0900.0860.0850.0820.0890.0930.0980.0870.0860.0850.1250.0850.0850.0940.0850.0850.0850.0850.0860.0860.0990.0930.0870.0850.0970.0930.0880.0860.0870.0850.0820.0920.0880.0850.1020.1040.0990.1000.0860.0880.0920.0930.0860.0860.0880.0880.0860.0880.0920.0850.1070.1340.0860.0980.0860.0880.0870.0850.0850.0850.0870.0850.0850.0860.0850.0900.0910.0850.0900.0890.1100.0930.0870.0900.0870.0890.0980.0870.0850.0870.0860.0880.0920.0850.0910.0850.0950.0870.0850.0860.1080.0860.0890.0860.0850.0860.0820.0850.0880.0930.0850.0860.0860.0000.0000.0000.0000.0000.0000.0000.0100.0100.0120.0000.0000.0290.0000.0000.0000.0000.0001.0000.0890.0000.0130.0000.0000.0000.0000.0000.0000.0180.0000.0080.0000.000
revenue_nan1.0000.0510.0000.0160.0000.0180.0160.0000.0000.0390.0000.0040.0340.1850.1270.1780.1410.1820.1280.1430.1710.1270.2670.1800.2390.1320.1530.1450.1580.1260.1360.2160.2500.1910.2250.1420.3450.0230.3200.2100.1220.8380.8370.8370.8400.8370.8370.8380.8370.8370.8360.8370.8370.8370.8380.8360.8380.8370.8370.8370.8370.8380.8370.8370.8360.8370.8400.8370.8370.8370.8370.8380.8370.8370.8380.8370.8370.8370.8370.8370.8370.8380.8380.8370.8370.8370.8380.8380.8370.8370.8370.8360.8370.8370.8370.8370.8370.8370.8380.8370.8370.8380.8390.8390.8370.8380.8380.8370.8380.8380.8370.8370.8380.8370.8370.8370.8370.8370.8370.8370.8370.8370.8370.8370.8370.8370.8370.8380.8370.8370.8380.8380.8370.8390.8370.8370.8370.8380.8370.8370.8370.8370.8370.8370.8370.8380.8370.8370.8370.8370.8370.8370.8370.8370.8370.8370.8370.8360.8370.8370.8380.8370.8370.8370.0600.0690.0590.0370.0110.1270.0160.0000.0000.1640.1380.2980.3610.2720.4030.4610.1800.6210.0891.0000.1050.0690.0000.0110.0090.0000.0170.0250.0380.0200.0560.0000.000
state0.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0940.0770.1040.0670.0790.0820.0740.0950.0820.0870.0870.0950.0940.0750.1190.0760.0810.0740.0680.0760.0940.0760.0360.3410.0920.2090.1100.1030.0900.0540.0680.0680.0540.0390.0680.0720.0550.0900.0900.0430.0760.0600.0900.0550.1050.0550.1040.0000.0650.0630.1930.0900.0580.0650.0860.0700.0690.0460.0890.0860.0390.0600.0710.0540.0900.1300.0560.0450.0580.0670.0640.0620.0590.0720.0960.0620.0550.0600.0900.0760.0610.0650.0850.0750.0700.0820.0470.0710.0690.0830.0520.0630.0770.0570.0610.0730.0610.0460.0630.0720.0510.0740.0590.0680.0710.0620.0550.0200.0620.0180.0000.0350.0000.0800.0720.0570.0830.0610.0720.0490.0800.0660.0560.0600.0720.0530.0670.0750.0770.0890.0660.0780.0640.0000.0760.0610.0900.0570.0660.0960.0500.0560.0900.0510.0900.0550.0570.0650.0630.0640.0630.0780.0570.0450.0000.0270.0900.0280.0580.0000.0560.0830.0560.0930.0730.0000.0680.0000.0650.0000.1051.0000.9960.9950.9950.9950.9950.9950.9951.0000.9950.9950.9950.995
state_top0.0480.3100.2170.1140.2200.2130.1840.3260.1190.1090.1560.4950.9840.1330.1060.1320.1040.1040.1070.1080.1100.1400.1230.1030.1580.1200.1020.1370.1040.1210.1040.1040.1050.1020.1110.1010.2680.3520.1040.3470.1440.0450.0440.0480.0580.0460.0380.0470.0310.0430.0460.0240.0390.0250.0490.0460.0430.0360.0380.0350.0280.0490.0420.0290.0460.0580.0600.0780.0420.0430.0260.1010.0480.0380.0320.0380.0290.0240.0250.0390.0340.0840.0970.0370.0380.0660.0670.0860.0590.0450.0410.0460.0640.0510.0480.0940.0730.0730.0730.0330.0580.0400.0620.0500.0390.0540.0560.0460.0500.0680.0400.0540.0690.0260.0510.0460.0350.0420.0330.0680.0370.0620.0380.0300.0320.0280.0380.0610.0300.0560.0620.0700.0300.0980.0540.0330.0440.0870.0380.0530.0580.0600.0450.0480.0510.0460.0280.0730.0380.0260.0410.0720.1040.0530.0550.0240.0380.0460.0370.0380.0540.0330.0460.0490.0640.0920.0440.0440.0310.0410.0000.0660.0410.0560.0500.1840.1700.0440.0160.0510.0000.0470.0130.0690.9961.0000.9990.9990.9990.9990.9990.9990.9990.9990.9990.9990.999
state_top_CA0.0540.3930.0860.0450.0880.0850.0730.1300.0470.0430.0620.1980.3940.1330.0820.0850.0740.0790.0850.0940.0750.0960.0850.0740.1140.1170.0740.1330.0770.1210.0740.0750.0800.0760.0820.0760.0200.0940.0600.1250.0750.0090.0000.0000.0000.0000.0050.0000.0000.0000.0000.0000.0070.0000.0000.0000.0000.0000.0000.0000.0200.0000.0000.0000.0000.0450.0000.0680.0000.0000.0000.0620.0000.0020.0000.0000.0000.0000.0000.0130.0000.0580.0610.0020.0210.0290.0480.0860.0640.0360.0080.0000.0560.0510.0000.0840.0650.0550.0730.0070.0330.0000.0000.0000.0000.0080.0000.0130.0000.0360.0000.0360.0360.0000.0400.0280.0120.0170.0000.0380.0210.0480.0000.0050.0060.0200.0000.0000.0020.0000.0610.0560.0150.0980.0360.0090.0200.0000.0050.0000.0000.0000.0000.0000.0000.0000.0200.0610.0070.0000.0270.0530.1110.0490.0580.0000.0000.0000.0000.0000.0070.0000.0000.0000.0230.0780.0000.0100.0000.0000.0040.0000.0340.0880.0110.0870.0750.0130.0210.0000.0000.0000.0000.0000.9950.9991.0000.0560.0620.0480.0660.0750.4000.1380.1850.0830.057
state_top_CO0.0410.1100.0210.0000.0210.0200.0160.0350.0000.0000.0110.0560.1130.0000.0000.0000.0000.0000.0000.0000.0000.0400.0510.0000.0210.0000.0000.0110.0020.0000.0000.0000.0000.0000.0000.0000.0000.0420.0100.0490.0000.0000.0200.0000.0000.0010.0000.0000.0000.0370.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0230.0070.0000.0060.0160.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0190.0000.0000.0170.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0120.0000.0000.0000.0000.0000.0000.0160.0000.0000.0000.0000.0000.0000.0000.0000.0130.0000.0000.0000.0000.0000.0000.0240.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0120.0000.0000.0250.0000.0000.0000.0220.0000.0100.0200.0000.0000.0000.0000.0080.0000.0110.9950.9990.0561.0000.0110.0020.0130.0170.1150.0370.0520.0190.009
state_top_FL0.0000.1220.0240.0040.0240.0230.0190.0390.0060.0010.0140.0620.1250.0190.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0070.0000.0000.0000.0130.0000.0000.0140.0000.0150.0000.0270.0610.0000.0640.0000.0000.0000.0000.0000.0000.0160.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0160.0000.0000.0000.0000.0090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0000.0000.0050.0000.0120.0000.0000.0000.0000.0000.0070.0000.0000.0100.0000.0000.0000.0000.0000.0000.0270.0050.0030.0000.0140.0000.0000.0000.0000.0000.0000.0000.0100.0000.0000.0000.0150.0180.0000.0000.0000.0000.0000.0000.0000.0100.0000.0000.0000.0000.0000.0120.0000.0000.0000.0160.0000.0030.0240.0280.0000.0000.0150.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0250.0070.0000.0000.0000.0000.0090.0210.0000.0340.0000.0000.0000.0000.0000.0010.0000.0000.0000.0090.9950.9990.0620.0111.0000.0070.0160.0190.1270.0420.0570.0220.012
state_top_GA0.0000.0950.0170.0000.0170.0160.0120.0300.0000.0000.0070.0480.0990.0190.0250.0260.0190.0180.0240.0230.0310.0210.0220.0200.0180.0200.0180.0180.0210.0200.0190.0270.0210.0180.0190.0180.0130.0310.0000.0290.0190.0000.0250.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0320.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0000.0000.0000.0000.0510.0000.0080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0130.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0090.0000.0000.0000.0060.0000.0000.0090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0130.0000.0000.0000.0000.0000.0060.0090.0000.0000.0110.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0160.0070.0000.0210.0140.0210.0270.0000.0160.0000.0000.0000.0000.0380.0150.0000.0000.0000.0000.0000.0000.0000.0000.0000.9950.9990.0480.0020.0071.0000.0090.0130.1000.0320.0450.0150.003
state_top_IL0.0000.1300.0260.0070.0260.0250.0210.0420.0080.0050.0160.0660.1330.0210.0140.0310.0170.0160.0220.0150.0150.0180.0140.0140.0200.0150.0150.0140.0140.0150.0230.0140.0170.0150.0180.0160.0890.0620.0230.0430.0170.0000.0060.0050.0200.0060.0140.0060.0080.0080.0100.0040.0070.0040.0060.0100.0070.0060.0050.0080.0040.0030.0150.0050.0100.0030.0180.0140.0070.0050.0050.0100.0070.0090.0200.0080.0140.0040.0040.0130.0100.0180.0080.0030.0070.0100.0080.0150.0140.0050.0070.0100.0090.0040.0250.0030.0060.0230.0040.0120.0040.0090.0280.0190.0090.0070.0140.0090.0240.0040.0450.0040.0130.0040.0040.0140.0050.0060.0070.0050.0180.0110.0320.0040.0070.0040.0300.0160.0060.0320.0030.0030.0030.0090.0070.0050.0330.0150.0110.0040.0230.0150.0090.0040.0110.0200.0040.0050.0040.0040.0050.0040.0030.0110.0100.0040.0100.0100.0040.0050.0270.0090.0110.0040.0000.0000.0090.0000.0000.0000.0000.0000.0000.0000.0230.0250.0300.0000.0000.0090.0000.0000.0000.0170.9950.9990.0660.0130.0160.0091.0000.0210.1350.0450.0610.0240.014
state_top_MA0.0000.1480.0300.0110.0310.0300.0250.0480.0120.0100.0190.0750.1510.0470.0330.0370.0330.0410.0330.0330.0320.0670.0350.0330.0750.0330.0390.0360.0400.0330.0320.0450.0340.0400.0500.0340.0510.0350.0180.0190.0340.0190.0200.0380.0240.0240.0190.0230.0200.0310.0200.0190.0380.0190.0240.0200.0220.0190.0310.0210.0190.0320.0210.0190.0200.0210.0290.0180.0340.0360.0190.0240.0200.0210.0200.0200.0200.0190.0190.0230.0230.0200.0380.0250.0280.0200.0180.0180.0240.0180.0200.0200.0230.0230.0300.0320.0300.0200.0180.0250.0190.0180.0180.0190.0370.0220.0260.0370.0190.0320.0200.0190.0220.0180.0190.0200.0260.0250.0200.0200.0200.0360.0330.0190.0240.0190.0200.0190.0240.0360.0190.0180.0180.0280.0180.0200.0240.0190.0270.0250.0260.0210.0230.0330.0190.0190.0190.0230.0270.0190.0250.0240.0230.0200.0190.0190.0190.0200.0190.0210.0260.0260.0390.0270.0000.0240.0050.0000.0000.0060.0000.0000.0050.0220.0060.0380.0510.0120.0000.0000.0000.0290.0000.0250.9950.9990.0750.0170.0190.0130.0211.0000.1530.0510.0700.0290.017
state_top_Missing0.0710.9840.2190.1170.2220.2150.1860.3280.1220.1120.1580.4950.9840.1570.1430.1700.1400.1410.1390.1430.1500.1860.1540.1420.2110.1490.1410.1510.1430.1480.1410.1390.1410.1390.1510.1400.1890.3480.0200.3430.1390.0000.0390.0460.0630.0540.0300.0460.0210.0230.0180.0200.0160.0160.0510.0180.0490.0160.0240.0200.0180.0480.0400.0170.0180.0710.0660.0980.0380.0410.0170.1380.0160.0180.0200.0180.0240.0200.0160.0340.0270.1110.1280.0260.0290.0840.0790.0920.0350.0380.0310.0180.0720.0460.0450.1120.0840.0920.0830.0180.0690.0420.0590.0540.0260.0460.0620.0250.0490.0810.0160.0640.0840.0150.0340.0470.0300.0470.0190.0900.0160.0700.0170.0200.0230.0180.0240.0670.0160.0490.0590.0880.0250.1210.0560.0230.0350.1010.0300.0560.0640.0680.0430.0490.0500.0440.0180.0940.0370.0230.0260.0920.1200.0500.0560.0200.0250.0180.0200.0350.0640.0180.0230.0440.0340.0580.0280.0480.0030.0090.0030.0530.0400.1100.0470.1820.1640.0250.0110.0160.0000.0230.0180.0381.0000.9990.4000.1150.1270.1000.1350.1531.0000.2780.3730.1680.118
state_top_NY0.0000.2730.0590.0290.0600.0580.0500.0900.0310.0280.0420.1370.2740.0520.0610.0530.0530.0520.0540.0540.0520.0680.0530.0520.0560.0520.0520.0660.0520.0520.0550.0520.0540.0540.0520.0520.0000.1130.0570.1410.0520.0230.0190.0170.0210.0290.0170.0190.0200.0190.0190.0170.0320.0170.0170.0190.0210.0180.0180.0170.0170.0170.0170.0210.0190.0230.0210.0280.0170.0170.0210.0410.0190.0210.0170.0170.0190.0170.0170.0280.0180.0410.0420.0260.0170.0420.0260.0460.0190.0170.0450.0190.0280.0180.0220.0440.0320.0430.0340.0220.0320.0260.0180.0250.0210.0260.0190.0170.0170.0380.0180.0310.0450.0220.0370.0260.0210.0200.0170.0310.0190.0310.0180.0280.0170.0170.0250.0290.0180.0170.0370.0320.0170.0250.0240.0240.0170.0230.0250.0340.0310.0210.0170.0220.0310.0190.0170.0310.0170.0170.0290.0290.0490.0260.0260.0170.0250.0190.0170.0170.0220.0220.0170.0200.0260.0130.0100.0000.0220.0100.0000.0510.0000.0650.0110.0640.0510.0000.0000.0190.0000.0070.0000.0200.9950.9990.1380.0370.0420.0320.0450.0510.2781.0000.1280.0560.038
state_top_Other0.1090.3660.0800.0420.0820.0790.0680.1220.0430.0400.0570.1850.3670.0360.0300.0500.0300.0300.0470.0320.0550.0400.0310.0430.0590.0320.0290.0710.0340.0380.0430.0300.0370.0340.0380.0300.2210.1620.0640.1150.0320.0390.0440.0430.0670.0490.0420.0610.0460.0410.0410.0410.0410.0430.0630.0410.0520.0420.0400.0520.0410.0600.0520.0450.0410.0450.0690.0500.0430.0430.0410.0750.0400.0440.0410.0410.0410.0410.0430.0420.0420.0530.0670.0450.0410.0540.0580.0410.0430.0420.0410.0410.0410.0410.0480.0540.0450.0440.0450.0410.0540.0460.0700.0560.0440.0700.0590.0410.0610.0510.0410.0420.0540.0420.0420.0450.0410.0440.0400.0510.0440.0410.0420.0410.0410.0410.0420.0660.0440.0620.0430.0500.0420.0420.0410.0410.0420.0920.0460.0490.0550.0640.0540.0520.0590.0550.0410.0520.0460.0410.0410.0520.0410.0440.0410.0410.0430.0410.0430.0490.0490.0410.0430.0460.0420.0000.0180.0180.0000.0350.0190.0130.0100.0660.0000.0510.0610.0320.0000.0510.0050.0360.0080.0560.9950.9990.1850.0520.0570.0450.0610.0700.3730.1281.0000.0770.053
state_top_TX0.0000.1630.0340.0140.0340.0330.0280.0530.0150.0120.0220.0820.1660.0140.0000.0000.0030.0160.0000.0000.0000.0050.0000.0050.0000.0000.0000.0000.0000.0000.0200.0070.0000.0000.0000.0000.0130.0450.0000.0470.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0140.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.0110.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0120.0000.0000.0000.0000.0260.0000.0000.0000.0000.0160.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0350.0000.0000.0000.0000.0000.0000.0000.0000.0250.0000.0000.0000.0000.0090.0180.0160.0290.0000.0000.0100.0000.0000.0000.0000.0000.9950.9990.0830.0190.0220.0150.0240.0290.1680.0560.0771.0000.020
state_top_WA0.0540.1120.0210.0000.0220.0210.0160.0360.0020.0000.0120.0570.1160.0000.0000.0740.0400.0000.0000.0000.0130.0140.0610.0000.0450.0070.0100.0000.0000.0000.0000.0000.0000.0000.0000.0070.0450.0390.0050.0290.0000.0000.0000.0290.0000.0000.0140.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0190.0000.0000.0090.0000.0000.0000.0180.0000.0070.0000.0000.0000.0130.0000.0280.0000.0370.0000.0000.0000.0000.0000.0140.0110.0000.0000.0000.0000.0150.0230.0000.0000.0000.0090.0170.0000.0000.0120.0020.0000.0000.0000.0020.0000.0000.0000.0000.0110.0370.0000.0110.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0170.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.0090.0000.0190.0200.0000.0040.0310.0000.0210.0000.0000.0220.0050.0000.0130.0000.0000.0000.0000.0000.0160.0000.0000.0000.0000.0240.0000.0000.0150.0000.0000.0350.0310.0050.0000.0000.0000.0230.0000.0000.0000.0000.0230.0000.0000.0000.0220.0000.0040.0000.0000.0000.0000.9950.9990.0570.0090.0120.0030.0140.0170.1180.0380.0530.0201.000

Missing values

2023-12-31T13:29:59.895884image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-31T13:30:01.575667image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-31T13:30:03.260925image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

account_idis_current_customeris_self_serviceis_arr_over_12kcompany_revenue_bucketcountrystateindustry_groupedhas_crossbeam_datacrossbeam_product1_customercrossbeam_product2_customercrossbeam_product3_customercrossbeam_product4_customercrossbeam_product5_customercrossbeam_product6_customercrossbeam_product7_customercrossbeam_product8_customercrossbeam_product9_customercrossbeam_product10_customercrossbeam_product11_customercrossbeam_product12_customercrossbeam_product13_customercrossbeam_product14_customercrossbeam_product15_customercrossbeam_product16_customercrossbeam_product17_customercrossbeam_product18_customercrossbeam_product19_customercrossbeam_product20_customercrossbeam_product21_customercrossbeam_product22_customercrossbeam_product23_customerdnb_founded_time_groupedhas_hg_datahg_product_27hg_product_28hg_product_29hg_product_30hg_product_31hg_product_32hg_product_33hg_product_34hg_product_35hg_product_36hg_product_37hg_product_38hg_product_39hg_product_40hg_product_41hg_product_42hg_product_43hg_product_44hg_product_45hg_product_46hg_product_47hg_product_48hg_product_49hg_product_50hg_product_51hg_product_52hg_product_53hg_product_54hg_product_55hg_product_56hg_product_57hg_product_58hg_product_59hg_product_60hg_product_61hg_product_62hg_product_63hg_product_64hg_product_65hg_product_66hg_product_67hg_product_68hg_product_69hg_product_70hg_product_71hg_product_72hg_product_73hg_product_74hg_product_75hg_product_76hg_product_77hg_product_78hg_product_79hg_product_80hg_product_81hg_product_82hg_product_83hg_product_84hg_product_85hg_product_86hg_product_87hg_product_88hg_product_89hg_product_90hg_product_91hg_product_92hg_product_93hg_product_94hg_product_95hg_product_96hg_product_97hg_product_98hg_product_99hg_product_100hg_product_101hg_product_102hg_product_103hg_product_104hg_product_105hg_product_106hg_product_107hg_product_108hg_product_109hg_product_110hg_product_111hg_product_112hg_product_113hg_product_114hg_product_115hg_product_116hg_product_117hg_product_118hg_product_119hg_product_120hg_product_121hg_product_122hg_product_123hg_product_124hg_product_125hg_product_126hg_product_127hg_product_128hg_product_129hg_product_130hg_product_131hg_product_132hg_product_133hg_product_134hg_product_135hg_product_136hg_product_137hg_product_138hg_product_139hg_product_140hg_product_141hg_product_142hg_product_143hg_product_144hg_product_145hg_product_146hg_product_147hg_product_148country_topstate_topcountry_top_AUcountry_top_BRcountry_top_CAcountry_top_DEcountry_top_FRcountry_top_GBcountry_top_INcountry_top_Missingcountry_top_NLcountry_top_Othercountry_top_USstate_top_CAstate_top_COstate_top_FLstate_top_GAstate_top_ILstate_top_MAstate_top_Missingstate_top_NYstate_top_Otherstate_top_TXstate_top_WAindustry_1. Tech - Computer systems design and related servicesindustry_2. Tech - Software Publisherindustry_3. Financeindustry_4. Consultingindustry_5. Retailindustry_6. Manufacturingindustry_7. Wholesale Tradeindustry_8. Healthcareindustry_9. Otherindustry_nanrevenue_Over 10Brevenue_Under 100Mrevenue_Under 10Brevenue_Under 10Mrevenue_Under 1Brevenue_Under 1Mrevenue_nanfounded_After 2000founded_Before 2000founded_nan
210011G00000rcirYQAQTrue1.0-1.0NaNNLNaN9. OtherFalse0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0After 2000False0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0NLMissingFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseTrueTrueFalseFalse
281001PY000002QFboYAGTrue1.0-1.0NaNUSMA5. RetailFalse0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0NaNFalse0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0USMAFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseTrue
473001Ho000015TLndIAGTrue1.0-1.0NaNGBNaNNaNFalse0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0NaNFalse0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0GBMissingFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseTrueFalseFalseTrue
613001Ho00001AhSYVIA3True1.0-1.0NaNGBNaN9. OtherFalse0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0NaNFalse0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0GBMissingFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseTrue
10010011G0000121xqjQAATrue1.0-1.0NaNNZNaNNaNFalse0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0NaNFalse0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0OtherMissingFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseTrueFalseFalseTrue
1776001Ho00001AiOKLIA3True1.0-1.0NaNDKNaN9. OtherFalse0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0NaNFalse0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0OtherMissingFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseTrue
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